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Готові списки джерел за темами / Electric power consumption Victoria Forecasting

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Добірка наукової літератури з теми "Electric power consumption Victoria Forecasting"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 31 липня 2025

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

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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Анотація:

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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Анотація:

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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Анотація:

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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Анотація:

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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Анотація:

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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Дисертації з теми "Electric power consumption Victoria Forecasting"

1

Huss, William Reed. "Load forecasting for electric utilities /." The Ohio State University, 1985. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487263399023837.

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2

Mangisa, Siphumlile. "Statistical analysis of electricity demand profiles." Thesis, Nelson Mandela Metropolitan University, 2013. http://hdl.handle.net/10948/d1011548.

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Анотація:

An electricity demand profile is a graph showing the amount of electricity used by customers over a unit of time. It shows the variation in electricity demand versus time. In the demand profiles, the shape of the graph is of utmost importance. The variations in demand profiles are caused by many factors, such as economic and en- vironmental factors. These variations may also be due to changes in the electricity use behaviours of electricity users. This study seeks to model daily profiles of energy demand in South Africa with a model which is a composition of two de Moivre type models. The mode

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3

Nyulu, Thandekile. "Weather neutral models for short-term electricity demand forecasting." Thesis, Nelson Mandela Metropolitan University, 2013. http://hdl.handle.net/10948/d1018751.

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Анотація:

Energy demand forecasting, and specifically electricity demand forecasting, is a fun-damental feature in both industry and research. Forecasting techniques assist all electricity market participants in accurate planning, selling and purchasing decisions and strategies. Generation and distribution of electricity require appropriate, precise and accurate forecasting methods. Also accurate forecasting models assist producers, researchers and economists to make proper and beneficial future decisions. There are several research papers, which investigate this fundamental aspect and attempt var-ious

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4

Si, Yau-li, and 史有理. "Forecasts of electricity demand and their implication for energy developments in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1990. http://hub.hku.hk/bib/B31976384.

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5

Cullen, Kathleen Ann. "Forecasting electricity demand using regression and Monte Carlo simulation under conditions of insufficient data." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=903.

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Анотація:

Thesis (M.S.)--West Virginia University, 1999.<br>Title from document title page. Document formatted into pages; contains x, 137 p. : ill., map Vita. Includes abstract. Includes bibliographical references (p. 99-107).

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6

Baba, Mutasim Fuad. "Intelligent and integrated load management system." Diss., Virginia Polytechnic Institute and State University, 1987. http://hdl.handle.net/10919/74744.

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Анотація:

The design, simulation and evaluation of an intelligent and integrated load management system is presented in this dissertation. The objective of this research was to apply modern computer and communication technology to influence customer use of electricity in ways that would produce desired changes in the utility's load shape. Peak clipping (reduction of peak load) using direct load control is the primary application of this research. The prototype computerized communication and control package developed during this work has demonstrated the feasibility of this concept. The load management

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7

Sapp, James Christopher. "Electricity Demand Forecasting in a Changing Regional Context: The Application of the Multiple Perspective Concept to the Prediction Process." PDXScholar, 1987. https://pdxscholar.library.pdx.edu/open_access_etds/574.

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In 1982, the Bonneville Power Administration (BPA), a marketer of hydroelectric power in the Pacific Northwest, found itself in a new role which required it to acquire power resources needed to meet the demands of the region's utilities. In particular, it had to deal with the Washington Public Power Supply System's nuclear plant cost escalations. In response, BPA prepared its first independent regional power forecast. The forecast development process was intricate and multidimensional and involved a variety of interested parties. Application of the Multiple Perspective Concept uncovers strengt

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8

Silva, Jesús, Naveda Alexa Senior, Palma Hugo Hernández, Núẽz William Niebles, and Núẽz Leonardo Niebles. "Temporary Variables for Predicting Electricity Consumption Through Data Mining." Institute of Physics Publishing, 2020. http://hdl.handle.net/10757/652132.

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Анотація:

In the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, it is feasible and convenient to predict consumption in the short term, from a few hours to a week. The hypothesis of the study is that the method used to present time variables to a prediction system of electricity consumption affects the results.

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9

Nigrini, Lucas Bernardo. "Developing a neural network model to predict the electrical load demand in the Mangaung municipal area." Thesis, [Bloemfontein?] : Central University of Technology, Free State, 2012. http://hdl.handle.net/11462/176.

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Thesis (D. Tech. (Engineering: Electric)) -- Central University of technology, 2012<br>Because power generation relies heavily on electricity demand, consumers are required to wisely manage their loads to consolidate the power utility‟s optimal power generation efforts. Consequently, accurate and reliable electric load forecasting systems are required. Prior to the present situation, there were various forecasting models developed primarily for electric load forecasting. Modelling short term load forecasting using artificial neural networks has recently been proposed by researchers.

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10

Sundin, Daniel. "Natural gas storage level forecasting using temperature data." Thesis, Linköpings universitet, Produktionsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-169856.

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Even though the theory of storage is historically a popular view to explain commodity futures prices, many authors focus on the oil price link. Past studies have shown an increased futures price volatility on Mondays and days when natural gas storage levels are released, which could both implicate that storage levels and temperature data are incorporated in the prices. In this thesis, the U.S. natural gas storage level change is studied as a function of the consumption and production. Consumption and production are furthered segmented and separately forecasted by modelling inverse problems tha

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Книги з теми "Electric power consumption Victoria Forecasting"

1

Willis, H. Lee. Spatial electric load forecasting. 2nd ed. Marcel Dekker, 2002.

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2

Estomin, Steven. Forecasted electric power demands for the Potomac Electric Power Company. The Program, 1988.

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3

National Association of Regulatory Utility Commissioners., ed. Electric power technology. National Association of Regulatory Utility Commissioners, 1990.

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4

W, Gellings Clark, and Barron W. L, eds. Demand forecasting for electric utilities. Fairmont Press, 1992.

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5

Soliman, S. A. Electrical load forecasting: Modeling and model construction. Butterworth-Heinemann, 2010.

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6

M, Bolet Adela, and Georgetown University. Center for Strategic and International Studies., eds. Forecasting U.S. electricity demand: Trends and methodologies. Westview Press, 1985.

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7

Northwest Power Planning Council (U.S.), ed. Draft forecast of electricity demand for the 5th Pacific Northwest conservation and electric power plan. Northwest Power Planning Council, 2002.

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8

Estomin, Steven. Forecasted electric energy consumption and peak demands for Maryland. Maryland Dept. of Natural Resources, 2006.

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9

Estomin, Steven. Forecasted electric energy consumption and peak demands for Maryland. Maryland Dept. of Natural Resources, 2003.

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10

Estomin, Steven. Forecasted electric energy consumption and peak demands for Maryland. Maryland Dept. of Natural Resources, 2006.

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Частини книг з теми "Electric power consumption Victoria Forecasting"

1

Seliverstova, Anastasiya V., Darya A. Pavlova, Slavik A. Tonoyan, and Yuriy E. Gapanyuk. "The Time Series Forecasting of the Company’s Electric Power Consumption." In Advances in Neural Computation, Machine Learning, and Cognitive Research II. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01328-8_24.

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2

Panchal, R., and B. Kumar. "Forecasting industrial electric power consumption using regression based predictive model." In Recent Trends in Communication and Electronics. CRC Press, 2021. http://dx.doi.org/10.1201/9781003193838-26.

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3

Kovan, Ibrahim, and Stefan Twieg. "Forecasting the Energy Consumption Impact of Electric Vehicles by Means of Machine Learning Approaches." In Electric Transportation Systems in Smart Power Grids. CRC Press, 2022. http://dx.doi.org/10.1201/9781003293989-3.

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4

Istomin, Stanislav, and Maxim Bobrov. "The Organization of Adaptive Control, Forecasting and Management of Electric Power Consumption of Electric Rolling Stock." In Lecture Notes in Networks and Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11058-0_154.

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5

Stütz, Sebastian, Andreas Gade, and Daniela Kirsch. "Promoting Zero-Emission Urban Logistics: Efficient Use of Electric Trucks Through Intelligent Range Estimation." In iCity. Transformative Research for the Livable, Intelligent, and Sustainable City. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_8.

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Анотація:

AbstractCritical success factors for the efficient use of electric trucks are the operational range and the total costs of ownership. For both range and efficient use, power consumption is the key factor. Increasing precision in forecasting power consumption and, hence, maximum range will pave the way for efficient vehicle deployment. However, not only electric trucks are scarce, but also is knowledge with respect to what these vehicles are actually technically capable of. Therefore, this article focuses on power consumption and range of electric vehicles. Following a discussion on how current

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6

Ashok Shivarkar, Sandip, and Sandeep Malik. "A Survey on Electric Power Demand Forecasting." In Recent Trends in Intensive Computing. IOS Press, 2021. http://dx.doi.org/10.3233/apc210236.

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Recently there has been tremendous change in use of the forecasting techniques due to the increase in availability of the power generation systems and the consumption of the electricity by different utilities. In the field of power generation and consumption it is important to have the accurate forecasting model to avoid the different losses. With the current development in the era of smart grids, it integrates electric power generation, demand and the storage, which requires more accurate and precise demand and generation forecasting techniques. This paper relates the most relevant studies on

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7

Mado, Ismit. "Electric Load Forecasting an Application of Cluster Models Based on Double Seasonal Pattern Time Series Analysis." In Forecasting in Mathematics - Recent Advances, New Perspectives and Applications [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.93493.

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Electricity consumption always changes according to need. This pattern deserves serious attention. Where the electric power generation must be balanced with the demand for electric power on the load side. It is necessary to predict and classify loads to maintain reliable power generation stability. This research proposes a method of forecasting electric loads with double seasonal patterns and classifies electric loads as a cluster group. Double seasonal pattern forecasting fits perfectly with fluctuating loads. Meanwhile, the load cluster pattern is intended to classify seasonal trends in a ce

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8

Dhupia, Bhawna, and M. Usha Rani. "Assessment of Electric Consumption Forecast Using Machine Learning and Deep Learning Models for the Industrial Sector." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7685-4.ch016.

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Power demand forecasting is one of the fields which is gaining popularity for researchers. Although machine learning models are being used for prediction in various fields, they need to upgrade to increase accuracy and stability. With the rapid development of AI technology, deep learning (DL) is being recommended by many authors in their studies. The core objective of the chapter is to employ the smart meter's data for energy forecasting in the industrial sector. In this chapter, the author will be implementing popular power demand forecasting models from machine learning and compare the resul

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9

Hari, Prakhar, Prachi Mishra, and Pooja Singh. "UNCERTAINTY ANALYSIS AND FORECASTING OF PV POWER PRODUCTION." In Futuristic Trends in Network & Communication Technologies Volume 2 Book 19. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2023. http://dx.doi.org/10.58532/v2bs19p3ch5.

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In today‘s time, the ecological condition and the energy supply has become critical around the world. The important reason for the growth and application of renewable energy sources is the limitation of non-renewable sources. The most ideal nonrenewable energy source is solar energy. The important feature in solar energy consumption patterns is photovoltaic power generation, but the output of photovoltaic power plant is irregular and changes frequently. The current work introduces an empirical ground framework for the analysis of uncertainty and forecasting of photovoltaic (PV) power generatio

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10

Gidom, Maysa. "Artificial Intelligent-Based Techniques in Solar Radiation Applications." In Solar Radiation - Enabling Technologies, Recent Innovations, and Advancements for Energy Transition [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.114133.

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The evolving smart grid emerges as a response to the challenges posed by the unreliability and inefficiency of the traditional electric grid. This transformation is crucial due to issues like the aging infrastructure and the intermittency of renewable energy sources, particularly solar radiation. The smart grid is anticipated to facilitate two-way power flows and introduce innovative technologies. This study explores the impact of smart grid technologies, particularly those supported by artificial intelligence (AI), on-demand load, future energy consumption, and energy management services. The

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Тези доповідей конференцій з теми "Electric power consumption Victoria Forecasting"

1

Babin, Artem S., Mikhail I. Baryshnikov, and Yuriy E. Gapanyuk. "Group Method of Data Handling (GMDH) in Forecasting Electric Power Consumption." In 2025 7th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE). IEEE, 2025. https://doi.org/10.1109/reepe63962.2025.10971009.

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2

Coban, Hasan Huseyin, Mohit Bajaj, Vojtech Blazek, Francisco Jurado, and Salah Kamel. "Forecasting Energy Consumption of Electric Vehicles." In 2023 5th Global Power, Energy and Communication Conference (GPECOM). IEEE, 2023. http://dx.doi.org/10.1109/gpecom58364.2023.10175761.

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3

Makoklyuev, B. I., A. S. Polizharov, and A. V. Antonov. "Methods and instruments for power consumption forecasting in electric power companies." In 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG). IEEE, 2015. http://dx.doi.org/10.1109/powereng.2015.7266331.

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4

Musaev, Timur, Marat Khabibulin, and Oleg Fedorov. "Multifactor Regression Model for Forecasting Production Power Consumption in Electric Grids." In 2023 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). IEEE, 2023. http://dx.doi.org/10.1109/icieam57311.2023.10139095.

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Gul, Mariam, Saad A. Qazi, and Waqar Ahmed Qureshi. "Incorporating economic and demographic variablesfor forecasting electricity consumption in Pakistan." In 2011 2nd International Conference on Electric Power and Energy Conversion Systems (EPECS). IEEE, 2011. http://dx.doi.org/10.1109/epecs.2011.6126852.

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Mlynek, Petr, Vaclav Uher, and Jiri Misurec. "Forecasting of Smart Meters Energy Consumption for Data Analytics and Grid Monitoring." In 2022 22nd International Scientific Conference on Electric Power Engineering (EPE). IEEE, 2022. http://dx.doi.org/10.1109/epe54603.2022.9814101.

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7

Yi Wang and Songqing Yu. "Annual electricity consumption forecasting with least squares support vector machines." In 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. IEEE, 2008. http://dx.doi.org/10.1109/drpt.2008.4523499.

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Ming Meng and Wei Shang. "Research on Annual Electric Power Consumption Forecasting Based on Partial Least-Squares Regression." In 2008 International Seminar on Business and Information Management (ISBIM 2008). IEEE, 2008. http://dx.doi.org/10.1109/isbim.2008.124.

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Filatova, Ekaterina S., Denis M. Filatov, Anastasia D. Stotckaia, and Grigoriy Dubrovskiy. "Time series dynamics representation model of power consumption in electric load forecasting system." In 2015 IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW). IEEE, 2015. http://dx.doi.org/10.1109/eiconrusnw.2015.7102256.

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Meng, Ming, and Wei Shang. "Chinese Annual Electric Power Consumption Forecasting Based on Grey Model and Global Best Optimization Method." In 2009 First International Workshop on Database Technology and Applications, DBTA. IEEE, 2009. http://dx.doi.org/10.1109/dbta.2009.126.

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