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Journal articles on the topic 'Electric power consumption Regression analysis'
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1
Karpenko, Sergey, and Nadezhda Karpenko. "Analysis and modeling of regional electric power consumption subject to influence of external factors." Energy Safety and Energy Economy 3 (June 2021): 12–17. http://dx.doi.org/10.18635/2071-2219-2021-3-12-17.
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Electric power consumption along with a large variety of factors affecting it can be forecasted and modelled by using econometric forecasting methods, including time series and correlation and regression analysis. For the purpose of this research, electric power consumption in the Moscow Region, Russia, was modelled with consideration of economic and climate factors based on 2019–2020 power usage data. A multiplicative model for regional electric power consumption and correlations between electric power consumption and an air temperature as well as a total number of cloudy days a month were built. The results will be helpful for analyzing and forecasting of processes involved in power consumption.
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Rakhmonov, I., N. Niyozov, and K. Li. "DEVELOPMENT OF CORRELATION AND REGRESSION MODELS OF ELECTRIC ENERGY INDICATORS OF THE EQUIPMENT WITH CONTINUOUS NATURE OF PRODUCTION." Technical science and innovation 2019, no. 4 (2019): 203–8. http://dx.doi.org/10.51346/tstu-01.19.4.-77-0039.
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The article presents an analysis of the use of correlation-regression analysis, which is based on the methods of mathematical statistics and probability theory in the study of the power consumption of enterprises with equipment of continuous production. On the basis of the annual power consumption schedule of the electric steel-smelting shop in a monthly time section, mathematical models have been developed for the power consumption parameters. And also, on the basis of statistical data with the use of a mathematical method, mathematical expressions were obtained for the electric power consumption and the specific consumption for the main equipment of the electric steel-smelting shop. In order to assess the adequacy of the developed mathematical models, mathematical models of the total and specific consumption of their power consumption are compared with actual data. The comparison results show high reliability of the power consumption modes of the main equipment of the facility in question. The analysis of the values of forecast errors with low error rates determines the adequacy of the developed mathematical models of the parameters of power consumption in terms of power consumption and specific consumption for the main equipment of the electric steel-smelting shop. In this regard, they can be used to determine the predicted values of the parameters of power consumption in electric steelmaking equipment.
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Bitimanova, Saltanat Serikbaevna, and Asel Asylbekovna Abdildaeva. "Algorithm for optimal control of electric power systems." Bulletin of Toraighyrov University. Energetics series, no. 4.2020 (December 17, 2020): 78–91. http://dx.doi.org/10.48081/wddo6475.
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This paper provides information about the current state of the energy system in Kazakhstan. Also, analyzing the technical condition of the structure of the Kazakhstan electro power station, a mathematical model for complex power systems is developed. Algorithms of control with Adams-Bashforth multistep method are developed. There has been conducted the analysis and assessment of significant factors affecting the forecasted dynamics of electric power consumption, built based on multivariate regression and cointegration models.
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Gracheva, E. I., and O. V. Naumov. "Application of fuzzy regression analysis method for determination of electric power losses in intrafactory power supply networks." Safety and Reliability of Power Industry 11, no. 4 (2019): 325–31. http://dx.doi.org/10.24223/1999-5555-2018-11-4-325-331.
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One of the main objectives of the development of modern industry in Russia, along with an increase in the absolute volumes of electric power (EP) production, is to strengthen control over its more rational use. Saving EP and reducing the cost of its transmission along power distribution networks is of great importance for the country's energy sector. In terms of their physical nature, in terms of production, transmission and consumption, EP losses are no different from EP served to consumers. Therefore, the assessment of power losses in electrical networks is based on the same economic principles as the assessment of energy served to consumers. EP losses have a significant impact on the technical and economic parameters of the network, since the cost of losses is included in the estimated cost (reduced costs) and cost price (annual operating costs) of EP transmission. The cost component of losses in the cost of EP transmission has a large proportion. The article presents the results of research on the possibility of application of fuzzy regression analysis for problems of assessment and prediction of electric power losses in intrafactory networks. Initial information on the network is uncertain to some extent, which complicates application of traditional methods. The calculation is presented for conventional and fuzzy regression models, along with estimation of error of these models. The relevance of application of fuzzy regression analysis methods is determined by the difficulty of obtaining reliable information about the circuit and regime parameters of intrafactory networks, the probabilistic nature of change of the modes, as well as a whole complex of affecting factors, which are generally challenging for quantitative assessment. Advantages of application of fuzzy regression analysis consist in obtaining confidence intervals of required variables (value of electric power losses) for schemes of networks with uncertain initial information on their parameters, which is characteristic of intrafactory power supply systems, and enables to consider dynamics of their variation.
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Istomin, Stanislav Gennadyevich, and Oleg Dmitrievich Yurasov. "Simulation model of heating system of DC electric-multiple units." Transport of the Urals, no. 4 (2020): 75–79. http://dx.doi.org/10.20291/1815-9400-2020-4-75-79.
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Most of the Russian Federation territory is located in the zone of long-term exposure to negative ambient temperatures. In this regard, a significant proportion of power consumption in the suburban traffic on the railways of the Russian Federation accounts for the operation of heating and air conditioning systems. Currently, Russian and foreign scientists are developing energy-saving methods and tools to reduce the power consumption for auxiliary needs of electric-multiple units. In this paper, the authors used the method of constructing simulation models in the MATLAB Simulink program in order to create an energy-saving heating and air conditioning system since this method allows you to explore various options for constructing the studied systems with lower financial and labour costs in comparison with the experimental method. In order to verify its adequacy the simulation model includes standard values of electric energy consumption for heating and air conditioning for various sections and operating conditions obtained by the authors earlier during the correlation and regression analysis of data from parameter recorders installed in electric-multiple units. The results of the study showed the adequacy of application of the developed simulation model for organizing the control of power consumption for heating and air conditioning of direct current electric-multiple units.
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Baklanov, Alexander, Nikolay Yesin, and Andrey Shilyakov. "ULL AND ENERGY EFFICIENCY ANALYSIS OF NEW ELECTRIC LOCOMOTIVES." Bulletin of scientific research results, no. 4 (December 17, 2017): 70–80. http://dx.doi.org/10.20295/2223-9987-2017-4-70-80.
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Objective: To study the specificities and parameters of the new, including innovative, freight and passenger electric locomotives, produced for domestic railways in the framework of the program of creating the new locomotives in 2004–2010. To analyze pull and energy efficiency parameters of direct current and alternating current electric locomotives. To estimate the maximum weight of trains and specific energy consumption of electric locomotives. To detect the advantages of new electric locomotives in comparison with those produced earlier. To develop guidelines on efficiency improvement of the new electric locomotives. Methods: Comparative analysis, methods of grade computations, linear regression analysis, power balance method. Results: The main design features and parameters of the new and earlier produced electric locomotives were studied, the former include the power of tractive motors, traction effort, as well as the speed at continuous rating of traction. The parameters of the new and earlier produced electric locomotives were compared. Key performance indicators of electric locomotives were analyzed, such as the maximum mass of a train and specific energy consumption on traction. The comparison of the above-mentioned indicators with performance indicators of earlier produced electric locomotives was given. According to calculation data and statistical data analysis the advantages of new electric locomotives were determined over those produced earlier. High performance of regenerative breaking was shown, specifically new electric locomotives. It was detected that in winter regeneration of electric energy was significantly reduced, in case of regenerative braking of passenger electric locomotives series EP1 with alternating current, as most of energy generated by tractive motors was spent on electric heating circuits of passenger cars. Guidelines on efficiency improvement of new electric locomotives were developed. Practical importance: The conditions in which new electric locomotives would implement the available advantages were determined, compared to those produced earlier. The elaborated offers make it possible to improve pull and energy efficiency of the new electric locomotives in operation.
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Krutsyak, Mykhailo. "FORECASTING DEMAND ON THE DOMESTIC ELECTRICITY MARKET ON THE BASIS OF THE RESULTS OF SOCIAL AND ECONOMIC INDICATORS DYNAMICS ANALYSIS." Economic Analysis, no. 28(3) (2018): 37–46. http://dx.doi.org/10.35774/econa2018.03.037.
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The works, which are devoted to the forecasting of demand for electric power, are analysed in this research. A number of these works is identified in order to use the available data. The influence of individual social and economic factors on the volume of annual electricity consumption in Ukraine is investigated. The use of forecasting of demand for electric energy data on the volume of gross domestic product on the parity of purchasing power, GDP energy intensity and the population of Ukraine for the period of 1991-2017 are substantiated, as well as the correlation between them. The annual volumes of electricity consumption are determined. It has been proposed the economic and mathematical model of forecasting and use of multiple regression equations. The method of reduction of the nonlinearity of the dynamics of the investigated factors is considered. We have compared the results, which are obtained after the use of this model, with the results of the available national forecasts.
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Istomin, Stanislav, and Aleksandr Shtraukhman. "Simulation model of the heating and air conditioning system of dc electric trains." E3S Web of Conferences 135 (2019): 02018. http://dx.doi.org/10.1051/e3sconf/201913502018.
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Most of the territory of the Russian Federation is located in the zone of long-term exposure to negative ambient temperatures. In this regard, in the suburban traffic on the railways of the Russian Federation, a significant proportion of the electric power falls on the operation of heating and air conditioning systems. Nowadays, Russia and the world are developing energy-saving methods and tools to reduce the energy consumption of auxiliary needs of electric trains. In this paper, the method of constructing simulation models in the MATLAB Simulink software was used to build an energy-saving heating and air conditioning system, since this method allows studying various options for building the studied systems with lower financial and labor costs in comparison with the experimental method. The correct selection and display of the parameters of the electric train interior will allow achieving the optimal values of energy consumption for heating and air conditioning of the electric trains. In order to verify its adequacy, the simulation model includes standard values of electric energy consumption for heating and conditioning electric trains for various sections and operating conditions, which were obtained earlier during the correlation and regression analysis of data from electric train parameter recorders. The results of the study showed the adequacy of the application of the developed simulation model for organizing the control of electric power consumption for heating and air conditioning of DC electric trains.
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Pokharel, Sugam, Pradip Sah, and Deepak Ganta. "Improved Prediction of Total Energy Consumption and Feature Analysis in Electric Vehicles Using Machine Learning and Shapley Additive Explanations Method." World Electric Vehicle Journal 12, no. 3 (2021): 94. http://dx.doi.org/10.3390/wevj12030094.
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Electric vehicles (EVs) have emerged as the green energy alternative for conventional vehicles. While various governments promote EVs, people feel “range anxiety” because of their limited driving range or charge capacity. A limited number of charging stations are available, which results in a strong demand for predicting energy consumed by EVs. In this paper, machine learning (ML) models such as multiple linear regression (MLR), extreme gradient boosting (XGBoost), and support vector regression (SVR) were used to investigate the total energy consumption (TEC) by the EVs. The independent variables used for the study include changing real-life situations or external parameters, such as trip distance, tire type, driving style, power, odometer reading, EV model, city, motorway, country roads, air conditioning, and park heating. We compared the ML models’ performance along with the error analysis. A pairwise correlation study showed that trip distance has a high correlation coefficient (0.87) with TEC. XGBoost had better prediction accuracy (~92%) or R2 (0.92). Trip distance, power, heating, and odometer reading were the most important features influencing the TEC, identified using the shapley additive explanations method.
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Soava, Georgeta, Anca Mehedintu, Mihaela Sterpu, and Eugenia Grecu. "The Impact of the COVID-19 Pandemic on Electricity Consumption and Economic Growth in Romania." Energies 14, no. 9 (2021): 2394. http://dx.doi.org/10.3390/en14092394.
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This paper analyzes the impact of the COVID-19 pandemic on economic growth and electricity consumption and investigates the hypothesis of the influence of this consumption on the gross domestic product (GDP) for Romania. Using time series on monthly electricity consumption and quarterly GDP and a multi-linear regression model, we performed an analysis of the evolution of these indicators for 2007–2020, a comparison between their behavior during the financial crisis vs. COVID-19 crisis, and empirically explore the relationships between GDP and electricity consumption or some of its components. The results of the analysis confirm that the shock of declining activity due to the COVID-19 pandemic had a severe negative impact on electric energy consumption and GDP in the first half of 2020, followed by a slight recovery. By using a linear regression model, long-term relationships between GDP and domestic and non-household electricity consumptions were found. The empirically estimated elasticity coefficients confirm the more important impact of non-household electricity consumption on GDP compared to the one of domestic electricity consumption. In the context of the COVID-19 pandemic, the results of the study could be useful for optimizing energy and economic growth policies at the national and European levels.
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Orji, Anthony, Jonathan E. Ogbuabor, Onyinye I. Anthony-Orji, Chinonso Okoro, and Daniel Osondu. "Analysis of ICT, Power Supply and Human Capital Development in Nigeria as an Emerging Market Economy." Studia Universitatis „Vasile Goldis” Arad – Economics Series 30, no. 4 (2020): 55–68. http://dx.doi.org/10.2478/sues-2020-0024.
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AbstractThis paper analyzed the impact of information and communication technology (ICT) and power supply on human capital development in Nigeria as an emerging market economy. The study adopted the Classical Linear Regression Model for the empirical analysis. The result showed that ICT, power supply (proxied by electricity consumption) and population impact positively on human capital development, while infant mortality has a negative impact on human capital development in Nigeria. The impact of ICT on school enrolment suggests that technology is fast evolving and new technologies are preferred to old ones. The study, therefore, recommended that Nigeria should follow in the trend of ICT globally in harnessing her human capital endowments. In conclusion, the Nigerian government should harness her ICT and electric power potentials and develop the human capital available to her to prevent the emigration of her human resource endowment to more resilient and promising economies.
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Hussnain, Syed Ali, Muhammad Farooq, Muhammad Amjad, et al. "Thermal Analysis and Energy Efficiency Improvements in Tunnel Kiln for Sustainable Environment." Processes 9, no. 9 (2021): 1629. http://dx.doi.org/10.3390/pr9091629.
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Kiln is a prime need in the ceramics industry, where energy loss is a major part which consumes about 60% production cost through thermal energy for different applications. Higher density of fired and tunnel kiln refractory material lowers the thermal diffusivity and the proper selection of fired material minimizes the energy loss along the kiln. In particular, this research analysed the results of a heat recovery system comprised of a metallic recuperator which gives around 8% energy savings in natural gas consumption. In this work, detailed power quality analysis of low-power factor motors of a tunnel kiln was carried out and a power factor improvement solution was suggested to save electrical energy with payback period of 0.8 y. The motor operating at a low-power factor consumes more reactive power which does not produce beneficial work. A low-power factor around 0.4 causes network power loss, increases in transformer loss and voltage drops. The solution with accumulative capacitance power of 148.05 uF was installed to achieve the power factor to 0.9. Flu gas analyzer was installed to monitor the range of O2 in pre-heating, oxidation, and firing zones of the kiln which should be ≥8% and 3%, respectively. Regression analysis for thermal energy consumption of a tunnel kiln is done to find the forecast thermal energy consumption. This analysis can be used to find operational efficiency, supporting decisions regarding dependent variable of thermal energy consumption and independent variable of production. This research is very helpful for the ceramics industry to mitigate the energy loss at SMEs as well as in mass production level.
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Saeed, Muhammad Salman, Mohd. Wazir Mustafa, Usman Ullah Sheikh, Attaullah Khidrani, and Mohd Norzali Haji Mohd. "THEFT DETECTION IN POWER UTILITIES USING ENSEMBLE OF CHAID DECISION TREE ALGORITHM." Science Proceedings Series 2, no. 2 (2020): 161–65. http://dx.doi.org/10.31580/sps.v2i2.1480.
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Theft of electricity and fraud in energy consumption billing are the primary concerns for Distribution System Operators . Because of those illegal activities, it is believed that billions of dollars are wasted each year. DSOs around the world continue to use conventional time consuming and inefficient methods for non-technical loss detection, particularly in underdeveloped countries . This research work attempts to solve the problems as mentioned above by designing an effective model for detecting electricity theft to classify fraudster customers in a power delivery system. The key motivation for this current study is to support the DSOs in their fight against the stealing of electricity. Initially, the proposed method uses the monthly energy customer consumption data obtained from Multan Electric Power Company (MEPCO) Pakistan to segregate fraudsters and honest customers. The Bagged Chi - square Automatic Interaction Detection (CHAID) based decision tree algorithm is then used to classify the honest and fraudster consumers.Furthermore, based on the mentioned metrics, the performance superiority of the Bagged CHAID-based NTL detection method is validated by comparing its efficacy with that of few well-known state-of-the-art machine learning algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM),) Logistic Regression (LR), Discriminant Analysis and Bayesian Network (BN).
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Kaur, Jasmeet, and Anju Bala. "Predicting power for home appliances based on climatic conditions." International Journal of Energy Sector Management 13, no. 3 (2019): 610–29. http://dx.doi.org/10.1108/ijesm-04-2018-0012.
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Purpose Power management in households has become the periodic issue for electric suppliers and household occupants. The number of electronic appliances is increasing day by day in every home with upcoming technology. So, it is becoming difficult for the energy suppliers to predict the power consumption for households at the appliance level. Power consumption in households depends on various factors such as building types, demographics, weather conditions and behavioral aspect. An uncertainty related to the usage of appliances in homes makes the prediction of power difficult. Hence, there is a need to study the usage patterns of the households appliances for predicting the power effectively. Design/methodology/approach Principal component analysis was performed for dimensionality reduction and for finding the hidden patterns to provide data in clusters. Then, these clusters were further being integrated with climate variables such as temperature, visibility and humidity. Finally, power has been predicted according to climate using regression-based machine learning models. Findings Power prediction was done based on different climatic conditions for electronic appliances in the residential sector. Different machine learning algorithms were implemented, and the result was compared with the existing work. Social implications This will benefit the society as a whole as it will help to reduce the power consumption and the electricity bills of the house. It will also be helpful in the reduction of the greenhouse gas emission. Originality/value The proposed work has been compared with the existing work to validate the current work. The work will be useful to energy suppliers as it will help them to predict the next day power supply to the households. It will be useful for the occupants of the households to complete their daily activities without any hindrance.
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Pamuła, Teresa, and Wiesław Pamuła. "Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning." Energies 13, no. 9 (2020): 2340. http://dx.doi.org/10.3390/en13092340.
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The estimation of energy consumption is an important prerequisite for planning the required infrastructure for charging and optimising the schedules of battery electric buses used in public urban transport. This paper proposes a model using a reduced number of readily acquired bus trip parameters: arrival times at the bus stops, map positions of the bus stops and a parameter indicating the trip conditions. A deep learning network is developed for deriving the estimates of energy consumption stop by stop of bus lines. Deep learning networks belong to the important group of methods capable of the analysis of large datasets—“big data”. This property allows for the scaling of the method and application to different sized transport networks. Validation of the network is done using real-world data provided by bus authorities of the town of Jaworzno in Poland. The estimates of energy consumption are compared with the results obtained using a regression model that is based on the collected data. Estimation errors do not exceed 7.1% for the set of several thousand bus trips. The study results indicate spots in the public transport network of potential power deficiency which can be alleviated by introducing a charging station or correcting the bus trip schedules.
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Chen, Ching Liang, and Yung Chung Chang. "Power Consumption Saving of Chiller Water System for Semiconductor Factory in Taiwan." Advanced Materials Research 314-316 (August 2011): 1492–501. http://dx.doi.org/10.4028/www.scientific.net/amr.314-316.1492.
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Recently, the semiconductor manufacturing industry has exhibited not only fast growth, but intense power consumption. Consequently, reducing power consumption is critical for running reliability. A view of literature reveals that the power consumption of facility system is 56.6 % in the fabs. Among all facility systems, chiller plants are the largest energy users, consuming 27.2 % of the total power consumption. Therefore, saving power consumption for chiller plants involves a considerable economic benefit. In addition, cooling the water temperature further improves the efficiency of chillers. Hence, this report analyzes the optimal temperature between the chiller and cooling tower. Currently, controlling the chiller and cooling tower are separate processes, though, in fact, they should not be. This is because the water cooling temperature affects the efficiency of the chiller. Each reduced degree of the chiller condenser temperature reduces the electrical power by approximately 2 % in the cooling tower, in contrast to the chiller. Therefore, the optimal water cooling water temperature must be analyzed. The analysis method in this report is linear regression. First, determine the equations of power consumption for the chiller and cooling tower with variables representing the water cooling temperature, water supply temperature of the chiller, and outdoor loading and wet-bulb temperatures. Second, add the coefficient of the same variable to obtain the total power consumption equation for the chiller and cooling tower. The result shows the relationships of power consumption with water cooling temperature under identical conditions of the water cooling temperature, water supply temperature of chiller, and outdoor loading and wet-bulb temperatures. Finally, use the differential method to determine the optimal water cooling temperature.
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Huang, Wenyang, Huiwen Wang, and Yigang Wei. "Endogenous or Exogenous? Examining Trans-Boundary Air Pollution by Using the Air Quality Index (AQI): A Case Study of 30 Provinces and Autonomous Regions in China." Sustainability 10, no. 11 (2018): 4220. http://dx.doi.org/10.3390/su10114220.
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China is experiencing severe environmental degradation, particularly air pollution. To explore whether air pollutants are spatially correlated (i.e., trans-boundary effects) and to analyse the main contributing factors, this research investigates the annual concentration of the Air Quality Index (AQI) and 13 polluting sectors in 30 provinces and autonomous regions across China. Factor analysis, the linear regression model and the spatial auto-regression (SAR) model are employed to analyse the latest data in 2014. Several important findings are derived. Firstly, the global Moran’s I test reveals that the AQI of China shows a distinct positive spatial correlation. The local Moran’s I test shows that significant high–high AQI agglomeration regions are found around the Beijing–Tianjin–Hebei area and the regions of low–low AQI agglomeration all locate in south China, including Yunnan, Guangxi and Fujian. Secondly, the effectiveness of the SAR model is much better than that of the linear regression model, with a significantly improved R-squared value from 0.287 to 0.705. A given region’s AQI will rise by 0.793% if the AQI of its ambient region increases by 1%. Thirdly, car ownership, steel output, coke output, coal consumption, built-up area, diesel consumption and electric power output contribute most to air pollution according to AQI, whereas fuel oil consumption, caustic soda output and crude oil consumption are inconsiderably accountable in raising AQI. Fourthly, the air quality in Beijing and Tianjin is under great exogenous influence from nearby regions, such as Hebei’s air pollution, and cross-boundary and joint efforts must be committed by the Beijing–Tianjin–Hebei region in order to control air pollution.
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Fan, Shurui, Zirui Li, Kewen Xia, and Dongxia Hao. "Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array." Sensors 19, no. 18 (2019): 3917. http://dx.doi.org/10.3390/s19183917.
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The gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for gas sensor array applications. The features extracted from the raw data utilizing principal component analysis (PCA) were used to complete random forest (RF) modeling, which enabled qualitative identification. Support vector regression (SVR), optimized by the particle swarm optimization (PSO) algorithm, was used to select hyperparameters C and γ to establish the optimal regression model for the purpose of quantitative analysis. Utilizing the dataset, we evaluated the effectiveness of our approach. Compared with logistic regression (LR) and support vector machine (SVM), the average recognition rate of PCA combined with RF was the highest (97%). The fitting effect of SVR optimized by PSO for gas concentration was better than that of SVR and solved the problem of hyperparameters selection.
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Schirmer, Pascal A., Iosif Mporas, and Akbar Sheikh-Akbari. "Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors." Energies 13, no. 9 (2020): 2148. http://dx.doi.org/10.3390/en13092148.
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A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.
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Foliaco, Blanca, Antonio Bula, and Peter Coombes. "Improving the Gordon-Ng Model and Analyzing Thermodynamic Parameters to Evaluate Performance in a Water-Cooled Centrifugal Chiller." Energies 13, no. 9 (2020): 2135. http://dx.doi.org/10.3390/en13092135.
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The Gordon-Ng models are tools that have been used to estimate and evaluate the performance of various types of chillers for several years. A 550 TR centrifugal chiller plant facility was available to collect data from July and September 2018. The authors propose rearranging variables of the traditional (GNU) model based on average electric consumption and through a thermodynamic analysis comparable to the original model. Furthermore, assumptions are validated. Then, by estimation of the parameters of the new model using least square fitting with field training data and comparing to the GNU model and Braun model (based on consumption), it was shown that the proposed model provides a better prediction in order to evaluate consumption of a centrifugal chiller in regular operation, by improving the coefficient of variation (CV), CV = 3.24% and R2 = 92.52% for a filtered sub-data. Through an algorithm built from steady-state cycle analysis, physical parameters (Sgen, Qleak,eq, R) were estimated to compare with the same parameters obtained by regression to check the influence of the interception term in the model. It was found that without an interception term, the estimated parameters achieve relative errors (ER) below 20%. Additional comparison between external and internal power prediction is shown, with CV = 3.57 % and mean relative error (MRE) of 2.7%, achieving better accuracy than GNU and Braun model.
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Huybrechts, Thomas, Philippe Reiter, Siegfried Mercelis, Jeroen Famaey, Steven Latré, and Peter Hellinckx. "Automated Testbench for Hybrid Machine Learning-Based Worst-Case Energy Consumption Analysis on Batteryless IoT Devices." Energies 14, no. 13 (2021): 3914. http://dx.doi.org/10.3390/en14133914.
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Batteryless Internet-of-Things (IoT) devices need to schedule tasks on very limited energy budgets from intermittent energy harvesting. Creating an energy-aware scheduler allows the device to schedule tasks in an efficient manner to avoid power loss during execution. To achieve this, we need insight in the Worst-Case Energy Consumption (WCEC) of each schedulable task on the device. Different methodologies exist to determine or approximate the energy consumption. However, these approaches are computationally expensive and infeasible to perform on all type of devices; or are not accurate enough to acquire safe upper bounds. We propose a hybrid methodology that combines machine learning-based prediction on small code sections, called hybrid blocks, with static analysis to combine the predictions to a final upper bound estimation for the WCEC. In this paper, we present our work on an automated testbench for the Code Behaviour Framework (COBRA) that measures and profiles the upper bound energy consumption on the target device. Next, we use the upper bound measurements of the testbench to train eight different regression models that need to predict these upper bounds. The results show promising estimates for three regression models that could potentially be used for the methodology with additional tuning and training.
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Smedegård, Ole Øiene, Thomas Jonsson, Bjørn Aas, Jørn Stene, Laurent Georges, and Salvatore Carlucci. "The Implementation of Multiple Linear Regression for Swimming Pool Facilities: Case Study at Jøa, Norway." Energies 14, no. 16 (2021): 4825. http://dx.doi.org/10.3390/en14164825.
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This paper presents a statistical model for predicting the time-averaged total power consumption of an indoor swimming facility. The model can be a powerful tool for continuous supervision of the facility’s energy performance that can quickly disclose possible operational disruptions/irregularities and thus minimize annual energy use. Multiple linear regression analysis is used to analyze data collected in a swimming facility in Norway. The resolution of the original training dataset was in 1 min time steps and during the investigation was transposed both by time-averaging the data, and by treating part of the dataset exclusively. The statistically significant independent variables were found to be the outdoor dry-bulb temperature and the relative pool usage factor. The model accurately predicted the power consumption in the validation process, and also succeeded in disclosing all the critical operational disruptions in the validation dataset correctly. The model can therefore be applied as a dynamic energy benchmark for fault detection in swimming facilities. The final energy prediction model is relatively simple and can be deployed either in a spreadsheet or in the building automation reporting system, thus the method can contribute instantly to keep the operation of any swimming facility within the optimal individual energy performance range.
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Schirmer, Pascal, and Iosif Mporas. "Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation." Sustainability 11, no. 11 (2019): 3222. http://dx.doi.org/10.3390/su11113222.
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In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.
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Waseem, Muhammad, Zhenzhi Lin, and Li Yang. "Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN." Big Data and Cognitive Computing 3, no. 3 (2019): 36. http://dx.doi.org/10.3390/bdcc3030036.
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Air Conditioners (AC) impact in overall electricity consumption in buildings is very high. Therefore, controlling ACs power consumption is a significant factor for demand response. With the advancement in the area of demand side management techniques implementation and smart grid, precise AC load forecasting for electrical utilities and end-users is required. In this paper, big data analysis and its applications in power systems is introduced. After this, various load forecasting categories and various techniques applied for load forecasting in context of big data analysis in power systems have been explored. Then, Levenberg–Marquardt Algorithm (LMA)-based Artificial Neural Network (ANN) for residential AC short-term load forecasting is presented. This forecasting approach utilizes past hourly temperature observations and AC load as input variables for assessment. Different performance assessment indices have also been investigated. Error formulations have shown that LMA-based ANN presents better results in comparison to Scaled Conjugate Gradient (SCG) and statistical regression approach. Furthermore, information of AC load is obtainable for different time horizons like weekly, hourly, and monthly bases due to better prediction accuracy of LMA-based ANN, which is helpful for efficient demand response (DR) implementation.
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Memon, Zain Anwer, Riccardo Trinchero, Paolo Manfredi, Flavio Canavero, and Igor S. Stievano. "Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks." Energies 13, no. 18 (2020): 4881. http://dx.doi.org/10.3390/en13184881.
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Today’s spread of power distribution networks, with the installation of a significant number of renewable generators that depend on environmental conditions and on users’ consumption profiles, requires sophisticated models for monitoring the power flow, regulating the electricity market, and assessing the reliability of power grids. Such models cannot avoid taking into account the variability that is inherent to the electrical system and users’ behavior. In this paper, we present a solution for the generation of a compressed surrogate model of the electrical state of a realistic power network that is subject to a large number (on the order of a few hundreds) of uncertain parameters representing the power injected by distributed renewable sources or absorbed by users with different consumption profiles. Specifically, principal component analysis is combined with two state-of-the-art surrogate modeling strategies for uncertainty quantification, namely, the least-squares support vector machine, which is a nonparametric regression belonging to the class of machine learning methods, and the widely adopted polynomial chaos expansion. Such methods allow providing compact and efficient surrogate models capable of predicting the statistical behavior of all nodal voltages within the network as functions of its stochastic parameters. The IEEE 8500-node test feeder benchmark with 450 and 900 uncertain parameters is considered as a validation example in this study. The feasibility and strength of the proposed method are verified through a systematic assessment of its performance in terms of accuracy, efficiency, and convergence, based on reference simulations obtained via classical Monte Carlo analysis.
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Kumar Singh, Sudhir, and Vijay Kumar Bajpai. "Estimation of operational efficiency and its determinants using DEA." International Journal of Energy Sector Management 7, no. 4 (2013): 409–29. http://dx.doi.org/10.1108/ijesm-03-2013-0009.
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Purpose – The purpose of this study is to benchmark the performance of state-owned coal-fired power plants (CFPPs) and test whether plant-specific knowledge in terms of quality of coal, size, age and make of plant contribute to an improvement in plant efficiency. Design/methodology/approach – The methodology that is utilized in the study follows a nonparametric approach of data envelopment analysis (DEA) with sensitivity analysis and Tobit regression model. The input-oriented DEA models are applied to evaluate the overall, pure technical and scale efficiencies of the CFPPs. Further, slack analysis is conducted to identify modes to improve the efficiency of the inefficient plants. Sensitivity analysis based on peer count and the removal of variables is carried out to identify the benchmark power plant. Through Tobit and bootstrap-truncated regression model, the paper investigates whether a plant's specific knowledge influences its efficiency. Findings – The DEA analysis demonstrates that nine plants are technically purely efficient.The slack analysis reveals that reducing the consumption of oil is the most effective way to improve the efficiency of inefficient plants. Mattur plant is the benchmark for most of the inefficient plants. Regression result suggests that quality of coal and size of plant significantly affect the inefficiency of the sample plants. Bharat Heavy Electrical Limited MAKE plant achieved higher efficiency in comparison to mixed MAKE. Originality/value – This study is one of the few published studies that benchmark the performance of state-owned CFPPs. This research carried out taking some new uncontrollable parameters of power plant utilities of India. Research work also identifies the possible causes of inefficiency and provides measures to improve the efficiency of the inefficient power plant.
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Kłos, Sławomir, Justyna Patalas-Maliszewska, Łukasz Piechowicz, and Krzysztof Wachowski. "Analysis and Predicting the Energy Consumption of Low-Pressure Carburising Processes." Energies 14, no. 12 (2021): 3699. http://dx.doi.org/10.3390/en14123699.
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The monitoring of the performance of heat treatment equipment has been the subject of a number of studies. This paper proposes and explores a new study on the models—and the monitoring thereof—for predicting the energy intensity of low-pressure carburisation processes using the DeepCaseMaster Evolution soaking furnace. For research purposes, 18 carburising experiments were performed with different carbon layers, at different input parameters, such as the number of cycles, time, temperature and average carburising pressure. Based on the research experiments conducted and statistical analysis, the influence of individual parameters on the energy consumption of the pump and heating systems was determined. Moreover, the models were verified on real data of low-pressure carburising processes. The innovativeness of the proposed solution is a combination of two areas: (1) defining and measurement of the parameters of the low-pressure carburising process; and (2) predicting the energy consumption of low-pressure carburising processes using correlation and regression analyses. The possibilities of using the results of this research in practice are demonstrated convincingly.
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Le, Yanfen, Shijialuo Jin, Hena Zhang, Weibin Shi, and Heng Yao. "Fingerprinting Indoor Positioning Method Based on Kernel Ridge Regression with Feature Reduction." Wireless Communications and Mobile Computing 2021 (January 9, 2021): 1–12. http://dx.doi.org/10.1155/2021/6631585.
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An important goal of indoor positioning systems is to improve positioning accuracy as well as reduce power consumption. In this paper, we propose an indoor positioning method based on the received signal strength (RSS) fingerprint. The proposed method used a certain criterion to select fixed access points (FPs) in an offline phase instead of an online phase for location estimation. Principal component analysis (PCA) was applied to reduce the features of the RSS measurements but retain the most information possible for establishing the positioning model. Then, a kernel-based ridge regression method was used to obtain the nonlinear relationship between the principal components of the RSS measures and the position of the target. We thoroughly investigated the performance of the proposed method in realistic wireless local area network (WLAN) and wireless sensor network (WSN) indoor environments and made comparisons with recently developed methods. The experimental results indicated that the proposed method was less dependent on the density of the reference points and had higher positioning accuracy than the commonly used positioning methods, and it adapts to different application environments.
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Pardo Albiach, Juan, Melanie Mir-Jimenez, Vanessa Hueso Moreno, Iván Nácher Moltó, and Javier Martínez-Gramage. "The Relationship between VO2max, Power Management, and Increased Running Speed: Towards Gait Pattern Recognition through Clustering Analysis." Sensors 21, no. 7 (2021): 2422. http://dx.doi.org/10.3390/s21072422.
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Triathlon has become increasingly popular in recent years. In this discipline, maximum oxygen consumption (VO2max) is considered the gold standard for determining competition cardiovascular capacity. However, the emergence of wearable sensors (as Stryd) has drastically changed training and races, allowing for the more precise evaluation of athletes and study of many more potential determining variables. Thus, in order to discover factors associated with improved running efficiency, we studied which variables are correlated with increased speed. We then developed a methodology to identify associated running patterns that could allow each individual athlete to improve their performance. To achieve this, we developed a correlation matrix, implemented regression models, and created a heat map using hierarchical cluster analysis. This highlighted relationships between running patterns in groups of young triathlon athletes and several different variables. Among the most important conclusions, we found that high VO2max did not seem to be significantly correlated with faster speed. However, faster individuals did have higher power per kg, horizontal power, stride length, and running effectiveness, and lower ground contact time and form power ratio. VO2max appeared to strongly correlate with power per kg and this seemed to indicate that to run faster, athletes must also correctly manage their power.
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Al-Omari, Zakaria, A. Hamzeh, Sadeq A. Hamed, A. Sandouk, and G. Aldahim. "A Mathematical Model for Minimizing Add-On Operational Cost in Electrical Power Systems Using Design of Experiments Approach." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 5 (2015): 948. http://dx.doi.org/10.11591/ijece.v5i5.pp948-956.
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One of the key functions of the Distribution System Operators (DSOs) of<br />electrical power systems (EPS) is to minimize the transmission and<br />distribution power losses and consequently the operational cost. This<br />objective can be reached by operating the system in an optimal mode which is performed by adjusting control parameters such as on-load tap changer (OLTC) settings of transformers, generator excitation levels, and VAR compensators switching. The deviation from operation optimality will result in additional losses and additional operational cost of the power system. Reduction of the operational cost increases the power system efficiency and provides a significant reduction in total energy consumption. This paper proposes a mathematical model for minimizing the additional (add-on) costs based on Design of Experiments (DOE). The relation between add-on operational costs and OLTC settings is established by means of regression statistical analysis. The developed model is applied to a 20-bustest network. The regression curve fitting procedure requires simulation experiments which have been carried out by the DigSilent PowerFactory 13.2 Program for performing network power flow. The results show the effectiveness of the model. The research work raises the importance the power system operation management of the EPS where the Distribution System Operator can avoid the add-on operational costs by continuous correction to get an operation mode close to optimality.
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Ahmad, Tanveer, and Qadeer Ul Hasan. "Detection of Frauds and Other Non-technical Losses in Power Utilities using Smart Meters: A Review." International Journal of Emerging Electric Power Systems 17, no. 3 (2016): 217–34. http://dx.doi.org/10.1515/ijeeps-2015-0206.
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Abstract Analysis of losses in power distribution system and techniques to mitigate these are two active areas of research especially in energy scarce countries like Pakistan to increase the availability of power without installing new generation. Since total energy losses account for both technical losses (TL) as well as non-technical losses (NTLs). Utility companies in developing countries are incurring of major financial losses due to non-technical losses. NTLs lead to a series of additional losses, such as damage to the network (infrastructure and the reduction of network reliability) etc. The purpose of this paper is to perform an introductory investigation of non-technical losses in power distribution systems. Additionally, analysis of NTLs using consumer energy consumption data with the help of Linear Regression Analysis has been carried out. This data focuses on the Low Voltage (LV) distribution network, which includes: residential, commercial, agricultural and industrial consumers by using the monthly kWh interval data acquired over a period (one month) of time using smart meters. In this research different prevention techniques are also discussed to prevent illegal use of electricity in the distribution of electrical power system.
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A., Nagesh*. "Energy Audit System for Households using Machine Learning." Regular issue 10, no. 7 (2021): 33–36. http://dx.doi.org/10.35940/ijitee.g8895.0510721.
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the growth in population and economics the global demand for energy is increased considerably. The large amount of energy demand comes from houses. Because of this the energy efficiency in houses in considered most important aspect towards the global sustainability. The machine learning algorithms contributed heavily in predicting the amount of energy consumed in household level. In this paper, a energy audit system using machine learning are developed to estimate the amount of energy consumed at household level in order to identify probable areas to plug wastage of energy in household. Each energy audit system is trained using one machine leaning algorithm with previous power consumption history of training data. By converting this data into knowledge, gratification of analysis of energy consumption is attained. The performance of energy audit Linear Regression system is 82%, Decision Tree system is 86% and Random Forest 91% are predicted energy consumption and the performance of learning methods were evaluated based on the heir predictive accuracy, ease of learning and user friendly characteristics. The Random Forest energy audit system is superior when compare to other energy audit system.
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Li, Chun Quan, and Xiao Le Kuang. "Structural Optimization of Ground Vias for 3D ICs." Advanced Materials Research 189-193 (February 2011): 1472–75. http://dx.doi.org/10.4028/www.scientific.net/amr.189-193.1472.
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Nowadays, IC manufacturing meet the challenges of physical limits, through silicon via(TSV) technology has increasingly become the focus of the microelectronics industry due to its shorter wiring route, better signal integrity, larger bandwidth, lower power consumption and smaller packaging size. In this paper, the transmission performance of TSV was analyzed and the impact of ground vias number, diameter and pitch with TSV on TSV transmission performance. A design of experiment (DOE) was established to investigate the impact of different ground vias parameter combinations on the transmission of TSV and the range analysis of the experiment results was executed. Based on the DOE, two regression equations were formed to estimate the electrical performances of TSV. From the two equations, the structure parameters were optimized, the S11 and S21 results of optimization parameter combination reduced 0.4dB and 0.15dB, respectively.
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Karacan, Rıdvan, Shahriyar Mukhtarov, İsmail Barış, Aykut İşleyen, and Mehmet Emin Yardımcı. "The Impact of Oil Price on Transition toward Renewable Energy Consumption? Evidence from Russia." Energies 14, no. 10 (2021): 2947. http://dx.doi.org/10.3390/en14102947.
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This research investigates the impact of oil price, income and carbon dioxide emissions on renewable energy consumption in Russia for the data period from 1990 to 2015, using the Vector Error Correction Models and the Canonical Cointegrating Regression method. This article is the only study conducting individual time-series analysis that emphasizes the effect of oil price on renewable energy consumption in the case of Russia. The results of empirical analysis conclude that oil price affects renewable energy consumption negatively. The negative oil price effects on renewable energy use can be interpreted as a sign of issue that stems from higher oil prices and slows the transition from conventional to renewable energy sources. Additionally, we found that there is a positive and statistically significant influence of real GDP per capita as a proxy of income on renewable energy consumption, whereas the carbon dioxide emissions have a negative and statistically insignificant influence on renewable energy consumption. Considering these empirical results, Russia, which has a significant share in energy production in the world, should focus on the use of renewable energy in order to maintain this superiority and its sustainability. The findings of this paper may be useful to policymakers and may help to contribute to existing literature for future research in the case of oil-exporting countries.
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Mărcuş, Gabriel, Vlad Iordache, Florin Iordache, and Anica Ilie. "Energy analysis of a CHP plant with internal combustion engines, for a district heating system, based on the information from the annual database." E3S Web of Conferences 85 (2019): 01012. http://dx.doi.org/10.1051/e3sconf/20198501012.
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Building a model for consumption, production and efficiency of a combined heat and power (CHP) system can bring important data for planning and management activity of such a technological system. The results of this study will show the overall efficiency of a real cogeneration plant over a year. The study is based on the information from the daily database of an economic operator, during 2012. The CHP plant, having reciprocating internal combustion engines (RICE) as prime movers, provides the thermal energy to the district heating system of a city from Romania with 129,368 habitants. RICE are operating in simultaneous mode or in partial load. The numerical model reveals the behavior of the daily thermal, electrical and global efficiency, accordingly to the partial load. The model was applied in both calculation assumptions: using the lower heating value and the higher heating value of the natural gas. A statistical analysis of efficiencies of the CHP plant was made. Was performed the statistical analysis of the database efficiency (also called real efficiency) with the global efficiency calculated by the model (also called modelled efficiency). Linear and multiple regression equations explain the variance of the real efficiency and of the modelled global efficiency.
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Khan, Zahoor Ali, Muhammad Adil, Nadeem Javaid, Malik Najmus Saqib, Muhammad Shafiq, and Jin-Ghoo Choi. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data." Sustainability 12, no. 19 (2020): 8023. http://dx.doi.org/10.3390/su12198023.
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Due to the increase in the number of electricity thieves, the electric utilities are facing problems in providing electricity to their consumers in an efficient way. An accurate Electricity Theft Detection (ETD) is quite challenging due to the inaccurate classification on the imbalance electricity consumption data, the overfitting issues and the High False Positive Rate (FPR) of the existing techniques. Therefore, intensified research is needed to accurately detect the electricity thieves and to recover a huge revenue loss for utility companies. To address the above limitations, this paper presents a new model, which is based on the supervised machine learning techniques and real electricity consumption data. Initially, the electricity data are pre-processed using interpolation, three sigma rule and normalization methods. Since the distribution of labels in the electricity consumption data is imbalanced, an Adasyn algorithm is utilized to address this class imbalance problem. It is used to achieve two objectives. Firstly, it intelligently increases the minority class samples in the data. Secondly, it prevents the model from being biased towards the majority class samples. Afterwards, the balanced data are fed into a Visual Geometry Group (VGG-16) module to detect abnormal patterns in electricity consumption. Finally, a Firefly Algorithm based Extreme Gradient Boosting (FA-XGBoost) technique is exploited for classification. The simulations are conducted to show the performance of our proposed model. Moreover, the state-of-the-art methods are also implemented for comparative analysis, i.e., Support Vector Machine (SVM), Convolution Neural Network (CNN), and Logistic Regression (LR). For validation, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Receiving Operating Characteristics Area Under Curve (ROC-AUC), and Precision Recall Area Under Curve (PR-AUC) metrics are used. Firstly, the simulation results show that the proposed Adasyn method has improved the performance of FA-XGboost classifier, which has achieved F1-score, precision, and recall of 93.7%, 92.6%, and 97%, respectively. Secondly, the VGG-16 module achieved a higher generalized performance by securing accuracy of 87.2% and 83.5% on training and testing data, respectively. Thirdly, the proposed FA-XGBoost has correctly identified actual electricity thieves, i.e., recall of 97%. Moreover, our model is superior to the other state-of-the-art models in terms of handling the large time series data and accurate classification. These models can be efficiently applied by the utility companies using the real electricity consumption data to identify the electricity thieves and overcome the major revenue losses in power sector.
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Komarnicka, Anna, and Anna Murawska. "Comparison of Consumption and Renewable Sources of Energy in European Union Countries—Sectoral Indicators, Economic Conditions and Environmental Impacts." Energies 14, no. 12 (2021): 3714. http://dx.doi.org/10.3390/en14123714.
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The use of energy is a precondition for global economic and civilisational development. However, the growing demand for energy is depleting traditional energy resources and, most importantly, causing environmental pollution, mainly through the emission of greenhouse gases. As energy is necessary for the functioning of all sectors of the economy, such as industry, services, transport as well as households, these sectors are the largest contributors to energy consumption. Renewable energy sources are an alternative to generating energy from conventional fossil fuels. The main objective of this paper was to determine and compare the level, trends and variation in energy consumption by different economic sectors in countries of the European Union in 2010–2019. An analysis of the share of renewable energy consumption in different economic sectors was also carried out, as well as an assessment of the relationship of these indicators with the level of economic development of the countries and environmental impacts in the form of greenhouse gas emissions from energy consumption. To explore the topics under discussion, a dozen of indicators have been considered in the article. The source of empirical data collected was the European Statistical Office. The researched period covered the years 2010–2019. The empirical data was statistically analysed. The article considers changes in the values of the studied indicators, differentiation between countries and the results of correlation and regression analysis. As shown by the data from 2010–2019, the countries of the European Union vary significantly in respect of primary and final energy consumption. The highest final energy consumption occurs in the transport sector, followed by slightly lower consumption in the industrial sector and households sector and the lowest but also significant consumption in the commercial and public services sector. Since 2010, total primary and final energy consumption has decreased in the EU (27) countries. Total energy consumption and consumption by individual sectors in modern economies of the EU (27) countries are reflected on the one hand in economic development and on the other—in exacerbation of adverse climate changes. Therefore, all EU Member States, aware of their energy consumption and their own contribution to environmental pollution, should take effective and sustainable corrective action in this area as soon as possible.
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Xu, Liquan, Yong Geng, Dong Wu, Chenyi Zhang, and Shijiang Xiao. "Carbon Footprint of Residents’ Housing Consumption and Its Driving Forces in China." Energies 14, no. 13 (2021): 3890. http://dx.doi.org/10.3390/en14133890.
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A large population size and rapid economic growth have resulted in a huge amount of housing consumption in China. Therefore, it is critical to identify the determinants of housing carbon footprint (CF) and prepare appropriate carbon mitigation measures. By employing the IPCC accounting method, input-output analysis and the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, this study aims to study the spatio-temporal patterns and identify the driving factors of housing CF. The results show that regional disparities and urban-rural differences existed during the period 2012–2017. The results of the extended STIRPAT model show that population scale and energy consumption per unit building area are the two dominant contributors to the housing CF increments in all areas. While, family size only shows significant negative impact in eastern and western regions, the per capita disposable income only induces higher housing CF in rural areas, and energy structure had a remarkable positive impact in urban area of western region and all rural areas. Policy recommendations are proposed to mitigate the overall housing CF, including; controlling population growth and promoting urbanization benefits; encouraging green consumption; optimizing household energy consumption structure, and; enhancing residential building energy management.
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Han, Oakyoung, and Jaehyoun Kim. "Uncertainty Analysis on Electric Power Consumption." Computers, Materials & Continua 68, no. 2 (2021): 2621–32. http://dx.doi.org/10.32604/cmc.2021.014665.
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Iancu, Ioana Ancuta, Cosmin Pompei Darab, and Stefan Dragos Cirstea. "The Effect of the COVID-19 Pandemic on the Electricity Consumption in Romania." Energies 14, no. 11 (2021): 3146. http://dx.doi.org/10.3390/en14113146.
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The COVID-19 pandemic obliged the Romanian government to take drastic measures to contain the virus. More than this, they imposed the heaviest restrictions in the EU. For more than a month, during the lockdown period, everything stopped: schools and universities had only online classes, national and international flights and gatherings were forbidden, and many restrictions for travel were imposed. This paper analyzes the changes that occurred in electricity consumption linked with economic growth, during the pandemic, in Romania. For a better understanding of the correlations between gross domestic product (GDP) and electricity consumption (EC) in different economic contexts, the period 2008–2020 was divided into three series: the 2008–2012 financial crisis and the post-crisis recovery period, the 2013–2019 period of economic growth, and the Q1–Q3 2020 pandemic period. Using correlation coefficients and regression analysis, the authors found that the GDP decoupled from EC in the first period. The increase in GDP led to an increase in the consumption of electricity and the electricity produced from RESs in the second period. In Q3 2020, the real GDP is different from the calculated GDP, due to the pandemic. In Romania, the electricity consumption decreased within the first nine months of the pandemic due to the economic contraction. The electricity that comes from coal and hydropower plants suffered the biggest decrease. If the electricity that comes from NRESs can be adapted to the economic demands, the quantity of electricity that comes from RESs will be influenced by the climate conditions.
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Adil, Muhammad, Nadeem Javaid, Umar Qasim, Ibrar Ullah, Muhammad Shafiq, and Jin-Ghoo Choi. "LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection." Applied Sciences 10, no. 12 (2020): 4378. http://dx.doi.org/10.3390/app10124378.
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The electrical losses in power systems are divided into non-technical losses (NTLs) and technical losses (TLs). NTL is more harmful than TL because it includes electricity theft, faulty meters and billing errors. It is one of the major concerns in the power system worldwide and incurs a huge revenue loss for utility companies. Electricity theft detection (ETD) is the mechanism used by industry and academia to detect electricity theft. However, due to imbalanced data, overfitting issues and the handling of high-dimensional data, the ETD cannot be applied efficiently. Therefore, this paper proposes a solution to address the above limitations. A long short-term memory (LSTM) technique is applied to detect abnormal patterns in electricity consumption data along with the bat-based random under-sampling boosting (RUSBoost) technique for parameter optimization. Our proposed system model uses the normalization and interpolation methods to pre-process the electricity data. Afterwards, the pre-processed data are fed into the LSTM module for feature extraction. Finally, the selected features are passed to the RUSBoost module for classification. The simulation results show that the proposed solution resolves the issues of data imbalancing, overfitting and the handling of massive time series data. Additionally, the proposed method outperforms the state-of-the-art techniques; i.e., support vector machine (SVM), convolutional neural network (CNN) and logistic regression (LR). Moreover, the F1-score, precision, recall and receiver operating characteristics (ROC) curve metrics are used for the comparative analysis.
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Beliaeva, Nataliia, Anton Petrochenkov, and Korinna Bade. "Data Set Analysis of Electric Power Consumption." European Researcher 61, no. 10-2 (2013): 2482–87. http://dx.doi.org/10.13187/er.2013.61.2482.
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Zhong, Yang, Aiwen Lin, Chiwei Xiao, and Zhigao Zhou. "Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data." Remote Sensing 13, no. 6 (2021): 1150. http://dx.doi.org/10.3390/rs13061150.
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In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran’s I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of “east-west” spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate.
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Yemelyanov, Olexandr, Anastasiya Symak, Tetyana Petrushka, et al. "Criteria, Indicators, and Factors of the Sustainable Energy-Saving Economic Development: The Case of Natural Gas Consumption." Energies 14, no. 18 (2021): 5999. http://dx.doi.org/10.3390/en14185999.
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To solve the contradiction between achieving long-term economic growth and reducing the consumption of certain types of resources, the concept of sustainable resource saving economic development must be put into practice. The purpose of this research is to establish criteria, develop indicators, and identify factors of the sustainable energy-saving economic development, as well as to test the developed theoretical provisions using the example of natural gas consumption by different countries. To achieve this goal, various methods were used, including economic and mathematical modeling, time series analysis, factor analysis, regression analysis, and so on. The criteria were formalized, according to which a certain type of economic development can be attributed to energy saving both at the level of the state economy as a whole and at the level of individual industries and enterprises. It was established that the formalized criteria of the sustainable energy-saving economic development have the form of chains of inequalities, and their application makes it possible to identify the general conditions for ensuring this type of development. The main properties of energy-saving economic development were identified. They include the pace of this development, its potential, balance, permanence, and other characteristics. Indicators that can be used to quantify these characteristics were developed. The factors influencing the scale and time characteristics of sustainable energy-saving economic development at the level of the state economy and that of industries and individual enterprises, were systematized. The dynamics of natural gas consumption in different countries was analyzed. The reasons for the lack of energy-saving natural gas economic development in some countries were identified. A quantitative assessment of the properties of this type of economic development by country was conducted. The influence of some factors on the parameters of the sustainable energy-saving natural gas economic development of countries was analyzed. The existence of a negative effect of the rebound in the consumption of natural gas was established at certain intervals in some countries. The obtained results provide an opportunity to increase the degree of understanding of the complex patterns that underlie the sustainable energy-saving economic development of states, industries, and enterprises. These results can also be used in the development of government programs to stimulate energy conservation.
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Heryana, G., S. Prasetya, M. Adhitya, and D. A. Sumarsono. "Power consumption analysis on large-sized electric bus." IOP Conference Series: Earth and Environmental Science 105 (January 2018): 012041. http://dx.doi.org/10.1088/1755-1315/105/1/012041.
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Peña-Guzmán, Carlos, and Juliana Rey. "Forecasting residential electric power consumption for Bogotá Colombia using regression models." Energy Reports 6 (February 2020): 561–66. http://dx.doi.org/10.1016/j.egyr.2019.09.026.
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Miyauchi, Hajime, Genta Tatsuguchi, and Tetsuya Misawa. "Regression Analysis of Electric Power Price in California Power Exchange." IEEJ Transactions on Power and Energy 124, no. 2 (2004): 199–206. http://dx.doi.org/10.1541/ieejpes.124.199.
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Liu, Jenny, Huaizheng Mu, Asad Vakil, et al. "Human Occupancy Detection via Passive Cognitive Radio." Sensors 20, no. 15 (2020): 4248. http://dx.doi.org/10.3390/s20154248.
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Human occupancy detection (HOD) in an enclosed space, such as indoors or inside of a vehicle, via passive cognitive radio (CR) is a new and challenging research area. Part of the difficulty arises from the fact that a human subject cannot easily be detected due to spectrum variation. In this paper, we present an advanced HOD system that dynamically reconfigures a CR to collect passive radio frequency (RF) signals at different places of interest. Principal component analysis (PCA) and recursive feature elimination with logistic regression (RFE-LR) algorithms are applied to find the frequency bands sensitive to human occupancy when the baseline spectrum changes with locations. With the dynamically collected passive RF signals, four machine learning (ML) classifiers are applied to detect human occupancy, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), and linear SVM with stochastic gradient descent (SGD) training. The experimental results show that the proposed system can accurately detect human subjects—not only in residential rooms—but also in commercial vehicles, demonstrating that passive CR is a viable technique for HOD. More specifically, the RFE-LR with SGD achieves the best results with a limited number of frequency bands. The proposed adaptive spectrum sensing method has not only enabled robust detection performance in various environments, but also improved the efficiency of the CR system in terms of speed and power consumption.
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Wójcik, Michał, Pia Brinkmann, Rafał Zdunek, et al. "Classification of Copper Minerals by Handheld Laser-Induced Breakdown Spectroscopy and Nonnegative Tensor Factorisation." Sensors 20, no. 18 (2020): 5152. http://dx.doi.org/10.3390/s20185152.
Full textAbstract:
Laser-induced breakdown spectroscopy (LIBS) analysers are becoming increasingly common for material classification purposes. However, to achieve good classification accuracy, mostly noncompact units are used based on their stability and reproducibility. In addition, computational algorithms that require significant hardware resources are commonly applied. For performing measurement campaigns in hard-to-access environments, such as mining sites, there is a need for compact, portable, or even handheld devices capable of reaching high measurement accuracy. The optics and hardware of small (i.e., handheld) devices are limited by space and power consumption and require a compromise of the achievable spectral quality. As long as the size of such a device is a major constraint, the software is the primary field for improvement. In this study, we propose a novel combination of handheld LIBS with non-negative tensor factorisation to investigate its classification capabilities of copper minerals. The proposed approach is based on the extraction of source spectra for each mineral (with the use of tensor methods) and their labelling based on the percentage contribution within the dataset. These latent spectra are then used in a regression model for validation purposes. The application of such an approach leads to an increase in the classification score by approximately 5% compared to that obtained using commonly used classifiers such as support vector machines, linear discriminant analysis, and the k-nearest neighbours algorithm.
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Domínguez, M., J. J. Fuertes, I. Díaz, A. A. Cuadrado, S. Alonso, and A. Morán. "Analysis of electric power consumption using Self-Organizing Maps." IFAC Proceedings Volumes 44, no. 1 (2011): 12213–18. http://dx.doi.org/10.3182/20110828-6-it-1002.02092.
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