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Готові списки джерел за темами / Electric power consumption Victoria Forecasting / Статті в журналах

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Electric power consumption Victoria Forecasting.

Статті в журналах з теми "Electric power consumption Victoria Forecasting"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 31 липня 2025

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1

Pepple, Tuanima. "Advanced Forecasting Techniques and Grid Management Strategies." International Journal of Electrical and Electronics Engineering Studies 10, no. 1 (2024): 1–18. http://dx.doi.org/10.37745/ijeees.13/vol10n1118.

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Energy forecasting is crucial for addressing challenges in data-rich smart grid (SG) systems, encompassing applications such as demand-side management, load shedding, and optimal dispatch. Achieving efficient forecasting with minimal prediction error remains a significant challenge due to the inherent uncertainty in SG data. This paper provides a comprehensive, application-focused review of advanced forecasting methods for SG systems, highlighting recent advancements in probabilistic deep learning (PDL).The review extensively examines traditional point forecasting methods, including statistica

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2

Lyutarevich, Alexander G. "Review of methods for prediction parameters of electricity quality and electric consumption." Yugra State University Bulletin 20, no. 2 (2024): 28–31. http://dx.doi.org/10.18822/byusu20240228-31.

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Subject of research: methods for predicting power consumption parameters. Purpose of research: to determine the optimal method for predicting power consumption and power quality parameters based on methods of analysis and synthesis. Object of research: methods for predicting parameters of power consumption and power quality based on neural networks. Main results of research: In recent years, forecasting power consumption and power quality parameters has become a very important topic, both from a technological and economic point of view. Forecasting electrical energy consumption ensures the mos

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3

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 bu

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4

Kakurina, A. V., A. S. Sizov, and Yu A. Khalin. "Cognitive Modelling and Forecasting of Electricity Consumption." Proceedings of the Southwest State University 27, no. 4 (2024): 44–61. http://dx.doi.org/10.21869/2223-1560-2023-27-4-44-61.

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Purpose of reseach. Development of a forecast model of energy consumption and assessment of factors influencing its consumption. The obtained forecast estimates of energy consumption will improve the quality and efficiency of management decisions at all levels of administrative management.Methods. The article presents an analytical review of the existing methods of cognitive modelling and forecasting of electric power consumption, the description of the software implementation of the information-computing system that allows to make a forecast of electric power consumption by the population of

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5

Gerossier, Alexis, Robin Girard, and George Kariniotakis. "Modeling and Forecasting Electric Vehicle Consumption Profiles." Energies 12, no. 7 (2019): 1341. http://dx.doi.org/10.3390/en12071341.

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The growing number of electric vehicles (EV) is challenging the traditional distribution grid with a new set of consumption curves. We employ information from individual meters at charging stations that record the power drawn by an EV at high temporal resolution (i.e., every minute) to analyze and model charging habits. We identify five types of batteries that determine the power an EV draws from the grid and its maximal capacity. In parallel, we identify four main clusters of charging habits. Charging habit models are then used for forecasting at short and long horizons. We start by forecasti

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6

Wu, Tan, De, et al. "Multiple Scenarios Forecast of Electric Power Substitution Potential in China: From Perspective of Green and Sustainable Development." Processes 7, no. 9 (2019): 584. http://dx.doi.org/10.3390/pr7090584.

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To achieve sustainable social development, the Chinese government conducts electric power substitution strategy as a green move. Traditional fuels such as coal and oil could be replaced by electric power to achieve fundamental transformation of energy consumption structure. In order to forecast and analyze the developing potential of electric power substitution, a forecasting model based on a correlation test, the cuckoo search optimization (CSO) algorithm and extreme learning machine (ELM) method is constructed. Besides, China’s present situation of electric power substitution is analyzed as

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7

Panda, Sujit Kumar, Alok Kumar Jagadev, and Sachi Nandan Mohanty. "Forecasting Methods in Electric Power Sector." International Journal of Energy Optimization and Engineering 7, no. 1 (2018): 1–21. http://dx.doi.org/10.4018/ijeoe.2018010101.

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Electric power plays a vibrant role in economic growth and development of a region. There is a strong co-relation between the human development index and per capita electricity consumption. Providing adequate energy of desired quality in various forms in a sustainable manner and at a competitive price is one of the biggest challenges. To meet the fast-growing electric power demand, on a sustained basis, meticulous power system planning is required. This planning needs electrical load forecasting as it provides the primary inputs and enables financial analysis. Accurate electric load forecasts

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8

Deng, Chengbin, Weiying Lin, Xinyue Ye, Zhenlong Li, Ziang Zhang, and Ganggang Xu. "Social media data as a proxy for hourly fine-scale electric power consumption estimation." Environment and Planning A: Economy and Space 50, no. 8 (2018): 1553–57. http://dx.doi.org/10.1177/0308518x18786250.

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Accurate forecasting of electric demand is essential for the operation of modern power system. Inaccurate load forecasting will considerably affect the power grid efficiency. Forecasting the electric demand for a small area, such as a building, has long been a well-known challenge. In this research, we examined the association between geotagged tweets and hourly electric consumption at a fine scale. All available geotagged tweets and electric meter readings were retrieved and spatially aggregated to each building in the study area. Comparing to traditional studies, the usage of geotagged tweet

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9

Khan, Anam-Nawaz, Naeem Iqbal, Atif Rizwan, Rashid Ahmad, and Do-Hyeun Kim. "An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings." Energies 14, no. 11 (2021): 3020. http://dx.doi.org/10.3390/en14113020.

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Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast econ

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10

Hoshimov, F. A., I. I. Bakhadirov, A. A. Alimov, and M. T. Erejepov. "Forecasting the electric consumption of objects using artificial neural networks." E3S Web of Conferences 216 (2020): 01170. http://dx.doi.org/10.1051/e3sconf/202021601170.

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The possibility of using artificial neural networks of the Matlab mathematical package for predicting the power consumption of objects is considered, the parameters that affect the power consumption are studied.

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11

Klyuev, Roman V., Irbek D. Morgoev, Angelika D. Morgoeva, et al. "Methods of Forecasting Electric Energy Consumption: A Literature Review." Energies 15, no. 23 (2022): 8919. http://dx.doi.org/10.3390/en15238919.

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Анотація:

Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on the means and methods of planning electricity production. Forecasting is one of the planning tools since the availability of an accurate forecast is a mechanism for increasing the validity of management decisions. This study provides an overview of the methods used to predict electricity supply requirements to different objects. The methods have been reviewed analytically, taking into account the forecast classification according to the anticipation period. In this way, the methods

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12

Song, Xinfu, Gang Liang, Changzu Li, and Weiwei Chen. "Electricity Consumption Prediction for Xinjiang Electric Energy Replacement." Mathematical Problems in Engineering 2019 (March 20, 2019): 1–11. http://dx.doi.org/10.1155/2019/3262591.

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Анотація:

In recent years, the phenomenon of wind and solar energy abandoned in Xinjiang’s new energy has become severe, the contradiction between the supply and demand of the power grid is obvious, and the proportion of power in the energy consumption structure is relatively low, thus hindering the development of Xinjiang’s green power. In this context, the focus of Xinjiang’s power has shifted to promote the development of electric energy replacement. Therefore, using the Xinjiang region as an example, we first select the important indicators such as the terminal energy substitution in Xinjiang, added

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13

Karpenko, S. M., N. V. Karpenko, and G. Y. Bezginov. "Forecasting of power consumption at mining enterprises using statistical methods." Mining Industry Journal (Gornay Promishlennost), no. 1/2022 (March 15, 2022): 82–88. http://dx.doi.org/10.30686/1609-9192-2022-1-82-88.

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Forecasting of electric power consumption with due account of assessed impact of various factors helps to make efficient technical and managerial decisions to optimize the electric power consumption processes, including preparation of bids for the wholesale electric power and capacity market. The article uses multivariate methods of statistical analysis and econometric methods based on time series analysis for model designing. The paper presents the results of developing the following models: a multifactor model of electrical power consumption using the regression analysis, the Principal Compo

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14

Son, Namrye, Seunghak Yang, and Jeongseung Na. "Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting." Applied Sciences 10, no. 18 (2020): 6489. http://dx.doi.org/10.3390/app10186489.

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Анотація:

Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict t

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15

Son, Namrye. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting." Sustainability 13, no. 22 (2021): 12493. http://dx.doi.org/10.3390/su132212493.

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Анотація:

Electricity demand forecasting enables the stable operation of electric power systems and reduces electric power consumption. Previous studies have predicted electricity demand through a correlation analysis between power consumption and weather data; however, this analysis does not consider the influence of various factors on power consumption, such as industrial activities, economic factors, power horizon, and resident living patterns of buildings. This study proposes an efficient power demand prediction using deep learning techniques for two industrial buildings with different power consump

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16

Tolegenova, G., A. Zakirova, and A. Astankevich. "Models and methods for forecasting electrical loads." BULLETIN of L.N. Gumilyov Eurasian National University. Technical Science and Technology Series 143, no. 2 (2023): 260–68. http://dx.doi.org/10.32523/2616-7263-2023-143-2-260-268.

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Анотація:

Currently, the prediction of electrical loads is an important task. On the basis of forecasts, the operating modes of stations, the network configuration are calculated, the efficiency and quality of electric power is estimated, the schedule of repair work is calculated, etc. The electric load forecasting model is one of the foresight tools for making management decisions when managing electric power systems. This article consists in the construction, evaluation and comparative study of various models for forecasting electricity consumption. The following approaches and methods in forecasting

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17

Yan, Ke, Xudong Wang, Yang Du, Ning Jin, Haichao Huang, and Hangxia Zhou. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy." Energies 11, no. 11 (2018): 3089. http://dx.doi.org/10.3390/en11113089.

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Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) n

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18

Shi, Jiarong, and Zhiteng Wang. "A Hybrid Forecast Model for Household Electric Power by Fusing Landmark-Based Spectral Clustering and Deep Learning." Sustainability 14, no. 15 (2022): 9255. http://dx.doi.org/10.3390/su14159255.

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Household power load forecasting plays an important role in the operation and planning of power grids. To address the prediction issue of household power consumption in power grids, this paper chooses a time series of historical power consumption as the feature variables and uses landmark-based spectral clustering (LSC) and a deep learning model to cluster and predict the power consumption dataset, respectively. Firstly, the investigated data are reshaped into a matrix and all missing entries are recovered by matrix completion. Secondly, the data samples are divided into three clusters by the

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19

Parate, Aaditi, and Sachin Bhoite. "Individual Household Electric Power Consumption Forecasting using Machine Learning Algorithms." International Journal of Computer Applications Technology and Research 8, no. 9 (2019): 371–76. http://dx.doi.org/10.7753/ijcatr0809.1007.

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20

Kassem, Sameh A., Abdulla H. A. EBRAHIM, Abdulla M. Khasan, and Alla G. Logacheva. "FORECASTING ELECTRIC CONSUMPTION OF THE ENTERPRISE USING ARTIFICIAL NEURAL NETWORKS." Tyumen State University Herald. Physical and Mathematical Modeling. Oil, Gas, Energy 7, no. 1 (2021): 177–93. http://dx.doi.org/10.21684/2411-7978-2021-7-1-177-193.

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Анотація:

Energy consumption has increased dramatically over the past century due to many factors, including both technological, social and economic factors. Therefore, predicting energy consumption is of great importance for many parameters, including planning, management, optimization and conservation. Data-driven models for predicting energy consumption have grown significantly over the past several decades due to their improved performance, reliability, and ease of deployment. Artificial neural networks are among the most popular data-driven approaches among the many different types of models today.

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21

Meng, Ming, Wei Shang, and Dongxiao Niu. "Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/243171.

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Monthly electric energy consumption forecasting is important for electricity production planning and electric power engineering decision making. Multiwindow moving average algorithm is proposed to decompose the monthly electric energy consumption time series into several periodic waves and a long-term approximately exponential increasing trend. Radial basis function (RBF) artificial neural network (ANN) models are used to forecast the extracted periodic waves. A novel hybrid growth model, which includes a constant term, a linear term, and an exponential term, is proposed to forecast the extrac

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22

Kuznetsov, V. H., M. O. Ivanov, and F. O. Fichoryak. "The power consumption rationing for own needs of traction substations." Science and Transport Progress, no. 21 (April 25, 2008): 61–68. http://dx.doi.org/10.15802/stp2008/15813.

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23

Obar, Eric Akpoviroro, Abdelwahed Touati, Mahmoud Almostafa Rabbah, Laince Pierre Moulebe, and Nabila Rabbah. "Supervised Learning for Energy Forecasting in Power Systems." WSEAS TRANSACTIONS ON POWER SYSTEMS 20 (March 28, 2025): 94–100. https://doi.org/10.37394/232016.2025.20.9.

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Анотація:

Since the inception of the electric grid in the 19th century, power systems have continuously evolved due to technological, industrial, legislative, demographic, environmental, and economic factors. With the advent of machine learning, monitoring and anticipating the evolutionary trends of the electric grid has become possible. This is facilitated by the convergence of vast data availability, sophisticated algorithms, and advanced computational capabilities. Our focus is on utilizing the supervised learning paradigm of machine learning for predictive analytics in power systems. Specifically, w

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24

Kim, Ji-Yoon, Jong-Hak Lee, Ji-Hyun Oh, and Jin-Seok Oh. "A Comparative Study on Energy Consumption Forecast Methods for Electric Propulsion Ship." Journal of Marine Science and Engineering 10, no. 1 (2021): 32. http://dx.doi.org/10.3390/jmse10010032.

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Efficient vessel operation may reduce operational costs and increase profitability. This is in line with the direction pursued by many marine industry stakeholders such as vessel operators, regulatory authorities, and policymakers. It is also financially justifiable, as fuel oil consumption (FOC) maintenance costs are reduced by forecasting the energy consumption of electric propulsion vessels. Although recent technological advances demand technology for electric propulsion vessel electric power load forecasting, related studies are scarce. Moreover, previous studies that forecasted the loads

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25

Bershadsky, Ilya, Sergey Dzhura, and Aurika Chursinova. "The use of artificial intelligence to predict electric power consumption of a power supply company." Science Bulletin of the Novosibirsk State Technical University, no. 4 (December 18, 2020): 7–16. http://dx.doi.org/10.17212/1814-1196-2020-4-7-16.

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The existing approaches to using artificial intelligence in training the neural network using the Neurosimulator 5.0 application to predict electricity consumption according to the data of the previous period are analyzed in this article. It is also concluded that it is advisable to develop this direction of calculations for forecasting and designing power supply systems. The article is devoted to the problem of choosing a model for forecasting electricity consumption when solving the problem of operational daily planning of electricity supplies in the wholesale market. The task of forecasting

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26

Liu, Chang, Yuanliang Zhang, Weisong Chen, Haitong Gu, Hui Li, and Shaoliang Chen. "A Short Term Forecasting Method for Regional Power Consumption Considering Related Factors." Journal of Physics: Conference Series 2195, no. 1 (2022): 012022. http://dx.doi.org/10.1088/1742-6596/2195/1/012022.

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Abstract Analysis and prediction of power consumption law is the basis of power grid planning and construction, and is also an effective guide for energy demand side management. With the rapid development of economy and the complex change of industrial structure in recent years, the internal structure of power demand is changing to some extent. Therefore, a short-term forecasting method of regional electricity consumption considering the related factors is proposed. Based on the analysis results, a short-term prediction model of regional electricity consumption considering the related factors

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27

Tay, K. G., Y. Y. Choy, and C. C. Chew. "Forecasting Electricity Consumption Using Fuzzy Time Series." International Journal of Engineering & Technology 7, no. 4.30 (2018): 342. http://dx.doi.org/10.14419/ijet.v7i4.30.22305.

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Electricity consumption forecasting is important for effective operation, planning and facility expansion of power system. Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development. There is a great development of Universiti Tun Hussein Onn Malaysia (UTHM) infrastructure since its formation in 1993. The development will be accompanied with the increasing demand of electricity. Hence, there is a need to forecast the UTHM electricity consumption for future decisions on generating electr

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28

Nasution, Aminulsyah, Badriana Badriana, and Andik Bintoro. "Application of The Combined Method in Inventory Forecasting Electricity at PT PLN (Persero) ULP Sibuhuan." International Journal of Engineering, Science and Information Technology 2, no. 4 (2022): 111–18. http://dx.doi.org/10.52088/ijesty.v2i4.348.

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The forecast of electricity consumption is a prediction of the use of electricity for the future by referring to the use of electricity in past data. Estimates of electricity consumption needs are intended to estimate how much electricity consumption is used by customers and must be provided by PT PLN (PERSERO) ULP SIBUHUAN as a provider of electrical energy services. Population growth occurs along with the development of an area, so it affects the demand for electricity and the need for electricity consumption. Load mapping must be done to maintain the continuity and distribution of electrica

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29

Yang, Yi, Zhihao Shang, Yao Chen, and Yanhua Chen. "Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting." Energies 13, no. 3 (2020): 532. http://dx.doi.org/10.3390/en13030532.

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As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neur

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30

Ragu, Vasanth, Seung-Weon Yang, Kangseok Chae, et al. "Analysis and Forecasting of Electric Power Energy Consumption in IoT Environments." International Journal of Grid and Distributed Computing 11, no. 6 (2018): 1–14. http://dx.doi.org/10.14257/ijgdc.2018.11.6.01.

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31

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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32

RODIONOV, Dmitrii G., Evgenii A. KONNIKOV, Oleg Yu BORISOV, Dar'ya A. KRYZHKO, and Irina A. SMIRNOVA. "A fuzzy approach to the regional electric power system's stability monitoring based on socially available information." Financial Analytics: Science and Experience 17, no. 1 (2024): 4–36. http://dx.doi.org/10.24891/fa.17.1.4.

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Анотація:

Subject. This article deals with the issues related to the stability of the region's electricity system. Objectives. The article aims to develop an original approach to monitoring the stability of the region's electric power system. Methods. For the study, we used a fuzzy logic approach. Results. The article proposes an algorithm for monitoring the stability of the region's electric power system based on socially accessible information, based on a fuzzy approach. The proposed forecasting research algorithm consists of five successive steps. The result of the forecasting was a polynomial functi

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33

Kalinchyk, Vasyl, Vitaliy Pobigaylo, Vitaliy Kalinchyk, Aleksandr Meita, and Olena Borychenko. "Combined models of electricity consumption." Bulletin of NTU "KhPI". Series: Problems of Electrical Machines and Apparatus Perfection. The Theory and Practice, no. 1 (7) (June 30, 2022): 34–37. http://dx.doi.org/10.20998/2079-3944.2022.1.07.

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Анотація:

The article investigates models and methods of electric load forecasting. It is shown that among the known methods of power consumption management, preference is given to those based on the use of forecast estimates. The analysis of works devoted to the issues of forecasting the processes of power consumption management systems of industrial enterprises is carried out. It is shown that it is expedient to use adaptive models as a basis for operative forecasting of loads of power supply systems of industrial enterprises. Analysis of adaptive models of electricity consumption forecasting based on

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34

Karshibayev, Asqar I., and Zavqiyor I. Jumayev. "Expanding the level of forecasting and operational planning of electric consumption at mining enterprise." E3S Web of Conferences 417 (2023): 03015. http://dx.doi.org/10.1051/e3sconf/202341703015.

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To calculate the energy consumption of open pits or enterprise as a whole without determining all components of the specific consumption of electricity it is advisable to use a method based on the use of models of power consumption regimes found by the results of multivariate regression analysis. The results of research can serve as a basis for making recommendations for increasing the level of forecasting and operational planning of power consumption in mining enterprises.

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35

Frikha, Majdi, Khaled Taouil, Ahmed Fakhfakh, and Faouzi Derbel. "Predicting Power Consumption Using Deep Learning with Stationary Wavelet." Forecasting 6, no. 3 (2024): 864–84. http://dx.doi.org/10.3390/forecast6030043.

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Power consumption in the home has grown in recent years as a consequence of the use of varied residential applications. On the other hand, many families are beginning to use renewable energy, such as energy production, energy storage devices, and electric vehicles. As a result, estimating household power demand is necessary for energy consumption monitoring and planning. Power consumption forecasting is a challenging time series prediction topic. Furthermore, conventional forecasting approaches make it difficult to anticipate electric power consumption since it comprises irregular trend compon

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36

Chandragiri Venkata Sai Dheeraj and Dr.C.Gulzar. "MODELING ELECTRIC VEHICLE ENERGY CONSUMPTION: A SYSTEMATIC AND CRITICAL REVIEW OF PREDICTION METHODS." International Journal of Information Technology and Computer Engineering 13, no. 2 (2025): 476–85. https://doi.org/10.62643/ijitce.2025.v13.i2.pp476-485.

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The dependability and stability of power networks depend on precise predictions of the energy use by electric vehicles. It is becoming increasingly important for utilities to accurately predict when and where demand spikes will occur in order to ensure an adequate supply, especially as the number of electric cars on the road continues to climb.One major obstacle to reliable demand forecasting for electric cars is the unpredictable and intermittent nature of their power use. Research into creating models capable of efficiently capturing and interpreting such complicated data is, hence, an expan

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37

Kalinchyk, Vasyl, Vitalii Pobigaylo, Vitalii Kalinchyk, Olena Borychenko, and Aleksandr Meita. "Application of neural networks for predicting electric load." Bulletin of NTU "KhPI". Series: Problems of Electrical Machines and Apparatus Perfection. The Theory and Practice, no. 2 (12) (December 26, 2024): 50–55. https://doi.org/10.20998/2079-3944.2024.2.10.

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The paper shows that the operational management of the power consumption regime is reduced to solving the problem of operational forecasting of the enterprise's load. The paper analyzes the works devoted to the forecasting of electric loads of power systems and industrial enterprises. It is shown that in order to achieve the required forecast accuracy, it is advisable to use adaptive forecasting procedures and, in particular, to use artificial neural networks. The use of artificial neural networks for forecasting the load of industrial enterprises is due to their properties, such as the abilit

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38

LING, S. H., F. H. F. LEUNG, L. K. WONG, and H. K. LAM. "COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR HOME ELECTRIC LOAD FORECASTING AND BALANCING." International Journal of Computational Intelligence and Applications 05, no. 03 (2005): 371–91. http://dx.doi.org/10.1142/s1469026805001659.

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The paper presents an electric load balancing system for domestic use. An electric load forecasting system, which is realized by a genetic algorithm-based modified neural network, is employed. On forecasting the home power consumption profile, the load balancing system can adjust the amount of energy stored in battery accordingly, preventing it from reaching certain practical limits. A steady consumption from the AC mains can then be obtained which will benefit both the users and the utility company. An example will be given to illustrate the merits of the forecaster, and its performance on ac

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39

Filipova-Petrakieva, S. K., and V. Dochev. "Short-Term Forecasting of Hourly Electricity Power Demand." Engineering, Technology & Applied Science Research 12, no. 2 (2022): 8374–81. http://dx.doi.org/10.48084/etasr.4787.

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The optimal use of electric power consumption is a fundamental indicator of the normal use of energy resources. Its quantity depends on the loads connected to the electric power grid, which are measured on an hourly basis. This paper examines forecasting methods for hourly electrical power demands for 7 days. Data for the period of 1 January 2015 and 24 December 2020 were processed, while the models' forecasts were tested on actual power load data between 25 and 31 December 2020, obtained from the Energy System Operator of the Republic of Bulgaria. Two groups of methods were used for the progn

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40

Ahmed, Nawzad M., and Ayad O. Hamdeen. "Predicting Electric Power Energy, Using Recurrent Neural Network Forecasting Model." Journal of University of Human Development 4, no. 2 (2018): 53. http://dx.doi.org/10.21928/juhd.v4n2y2018.pp53-60.

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Electricity is counted as a one of the most important energy sources in the world. It has played a main role in developing several sectors. In this study two types of electricity variables have been used, the first was the demand on power energy, and the second was the consumption or energy load in Sulaimani city. The main goal of the study was to construct an analytic model of the recurrent neural network (RNN) for both variables. This model has a great ability in detecting the complex patterns for the data of a time series, which is most suitable for the data under consideration. This model

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41

Saitov, S. R., N. D. Chichirova, A. A. Filimonova, and N. B. Karnitsky. "Forecasting Peak Hours for Energy Consumption in Regional Power Systems." ENERGETIKA. Proceedings of CIS higher education institutions and power engineering associations 67, no. 1 (2024): 78–91. http://dx.doi.org/10.21122/1029-7448-2024-67-1-78-91.

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. Electrical power is the second most important commodity in electrical energy markets. For consumers, the charged amount of “generator” power is determined as the average value of hourly consumption amounts on working days during peak hours in the region. The cost of power in some regions can reach 40 % of the final tariff, so reducing the load during peak hours by 10 % can lead to a decrease in monthly consumer payments by 3 %. However, such a way of saving money is not available to the consumer since the commercial operator of the wholesale market of electricity and capacity publishes the p

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42

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 vo

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43

Jarykbassov, Daniyar, Petr Lezhniuk, Iryna Hunko, Vladyslav Lysyi, and Lyubov Dobrovolska. "MACROMODELING OF LOCAL POWER SUPPLY SYSTEM BALANCE FORECASTING USING FRACTAL PROPERTIES OF LOAD AND GENERATION SCHEDULES." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 13, no. 3 (2023): 79–82. http://dx.doi.org/10.35784/iapgos.4457.

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A method of forecasting the balance of electricity consumption of urban development objects, civil purposes using discrete macromodels is proposed. We consider the power supply system (PSS) of the district, which is characterised by power supply from general-purpose power grids, as well as having its own generation of electricity from renewable energy sources (RES). Such a local electric power system (LES) under certain conditions can be operated as an independent balanced electrical facility. For optimal operation of the LES under these conditions, it is necessary to predict its power consump

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Moustakim, Mohammed Amine, Tamou Nasser, Najib Elkamoun, and Ahmed Essadki. "Prediction of electric power and load forcasting using LSTM technique for EMS." EPJ Web of Conferences 330 (2025): 03005. https://doi.org/10.1051/epjconf/202533003005.

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A designed electric load and energy forecasting is proposed for buildings. Two different forecasting models, one for electricity consumption (medium-term load forecasting MTLF) and another for electrical en- ergy (very short-term) are proposed, compared, and interpreted. To feed those prediction models, and depending on dataset quality, two available online websites are chosen. Forecasting models are developed by PYTHON programming language, using various libraries dedicated to machine learning projects, such as ’TensorFlow’, ’Kiras’ and ’Scikit-learn’, and others for the visualization of resu

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45

Vyshnevskyy, O. K., and L. M. Zhuravchak. "FORECASTING THE ELECTRICITY CONSUMPTION USING AN ENSEMBLE OF MACHINE LEARNING MODELS." Ukrainian Journal of Information Technology 6, no. 2 (2024): 20–29. https://doi.org/10.23939/ujit2024.02.020.

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The use of machine learning models for electricity consumption prediction for smart grid has been investigated. It was found that data pre-processing can improve the performance of the energy consumption prediction model, while machine learning algorithms can improve model prediction accuracy through the integration of multiple algorithms and hyperparameter optimization. It was found that the ensemble learning method can provide better prediction accuracy than each individual method by combining the strong features of different methods that have different structural characteristics. Based on t

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46

Zhang, Jiaan, Wenxin Liu, Zhenzhen Wang, and Ruiqing Fan. "Electric Vehicle Power Consumption Modelling Method Based on Improved Ant Colony Optimization-Support Vector Regression." Energies 17, no. 17 (2024): 4339. http://dx.doi.org/10.3390/en17174339.

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Accurate forecasting of electric vehicle (EV) power consumption per unit mileage serves as the cornerstone for determining diurnal variations in EV charging loads. To enhance the prediction accuracy of EV power consumption per unit mileage, this paper proposes a modelling method grounded in an improved Ant Colony Optimization-Support Vector Regression (ACO-SVR) framework. This method integrates the effects of both temperature and speed on the power consumption per unit mileage of EVs. Initially, we analyze the influence mechanism of driving speed and ambient temperature on EV power consumption

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Tang, Junci, Guanfu Wang, Zhiyuan Cai, et al. "Ultra short term load forecasting for different types of industrial parks with intelligent buildings." Journal of Physics: Conference Series 2378, no. 1 (2022): 012082. http://dx.doi.org/10.1088/1742-6596/2378/1/012082.

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Abstract For industrial parks with intelligent buildings, accurate forecasting of various load sizes may reduce the power supply pressure of the power grid. For industrial parks with intelligent buildings, considering the influence of weather factors and the dynamic electricity price game mechanism, the load forecasting of industrial parks often ignores the load of intelligent buildings and electric vehicles, resulting in insufficient satisfaction of residents in the buildings. The improved Attention-LSTM algorithm based on DBN structure is proposed. It takes into account the correlation betwe

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Amezquita, Herbert, Cindy P. Guzman, and Hugo Morais. "Forecasting Electric Vehicles’ Charging Behavior at Charging Stations: A Data Science-Based Approach." Energies 17, no. 14 (2024): 3396. http://dx.doi.org/10.3390/en17143396.

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The rising adoption of electric vehicles (EVs), driven by carbon neutrality goals, has prompted the need for accurate forecasting of EVs’ charging behavior. However, this task presents several challenges due to the dynamic nature of EVs’ usage patterns, including fluctuating demand and unpredictable charging durations. In response to these challenges and different from previous works, this paper presents a novel and holistic methodology for day-ahead forecasting of EVs’ plugged-in status and power consumption in charging stations (CSs). The proposed framework encompasses data analysis, pre-pro

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Klungsida, Nivadee, Pakin Maneechot, Narut Butploy, and Kanokwan Khiewwan. "Forecasting Energy Consumption from EV Station Charging Using RNN, LSTM and GRU Neural Network." Journal of Renewable Energy and Smart Grid Technology 19, no. 1 (2024): 1–6. http://dx.doi.org/10.69650/rast.2024.254636.

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The increase in electric vehicles (EVs) has resulted in a substantial escalation in electricity consumption. This increased demand puts more stress on the overall power system. The current study offers a method to predict energy usage patterns by looking closely at when electric vehicles typically need to charge during the day. After that, the collected data were used to create a predictive model using three different deep learning methods: Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs). This study employs data pertaining to electric

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Sanieva, Alina D. "LOAD FORECASTING MODELS FOR INTELLIGENT POWER GRID MANAGEMENT SYSTEMS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 11/7, no. 152 (2024): 104–14. https://doi.org/10.36871/ek.up.p.r.2024.11.07.011.

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The article is devoted to the analysis of electric load forecasting models for intelligent power grid management systems. Short-term and long-term approaches are consid-ered, as well as hybrid models combining machine learning and statistical methods to improve the accuracy of predictions. The difficulties encountered in the integration of renewable energy sources and the role of artificial intelligence in adaptive load management are described. The prospects of using intelligent systems to optimize energy consumption in real time are predicted.

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