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Relevant bibliographies by topics / Electric power consumption Victoria Forecasting

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Academic literature on the topic 'Electric power consumption Victoria Forecasting'

Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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Journal articles on the topic "Electric power consumption Victoria Forecasting"

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Gerossier, Alexis, Robin Girard, and George Kariniotakis. "Modeling and Forecasting Electric Vehicle Consumption Profiles." Energies 12, no. 7 (April 8, 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 forecasting day-ahead consumption scenarios for a single EV. By summing scenarios for a fleet of EVs, we obtain probabilistic forecasts of the aggregated load, and observe that our bottom-up approach performs similarly to a machine-learning technique that directly forecasts the aggregated load. Secondly, we assess the expected impact of the additional EVs on the grid by 2030, assuming that future charging habits follow current behavior. Although the overall load logically increases, the shape of the load is marginally modified, showing that the current network seems fairly well-suited to this evolution.

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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 (January 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 are helpful in formulating load management strategies in view of different emerging economic scenarios, which can be dovetailed with the development plan of the region. The objective of this article is to understand various long term electrical load forecasting techniques, to assess its applicability; and usefulness for long term electrical load forecasting for an isolated remote region, under different growth scenarios considering demand side management, price and income effect.

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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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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 (September 17, 2019): 371–76. http://dx.doi.org/10.7753/ijcatr0809.1007.

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Klyuev, Roman V., Irbek D. Morgoev, Angelika D. Morgoeva, Oksana A. Gavrina, Nikita V. Martyushev, Egor A. Efremenkov, and Qi Mengxu. "Methods of Forecasting Electric Energy Consumption: A Literature Review." Energies 15, no. 23 (November 25, 2022): 8919. http://dx.doi.org/10.3390/en15238919.

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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 used in operative, short-term, medium-term, and long-term forecasting have been considered. Both classical and modern forecasting methods have been identified when forecasting electric energy consumption. Classical forecasting methods are based on the theory of regression and statistical analysis (regression, autoregressive models); probabilistic forecasting methods and modern forecasting methods use classical and deep-machine-learning algorithms, rank analysis methodology, fuzzy set theory, singular spectral analysis, wavelet transformations, Gray models, etc. Due to the need to take into account the specifics of each subject area characterizing an energy facility to obtain reliable forecast results, power consumption modeling remains an urgent task despite a wide variety of other methods. The review was conducted with an assessment of the methods according to the following criteria: labor intensity, requirements for the initial data set, scope of application, accuracy of the forecasting method, the possibility of application for other forecasting horizons. The above classification of methods according to the anticipation period allows highlights the fact that when predicting power consumption for different time intervals, the same methods are often used. Therefore, it is worth emphasizing the importance of classifying the forecast over the forecasting horizon not to differentiate the methods used to predict electricity consumption for each period but to consider the specifics of each type of forecasting (operative, short-term, medium-term, long-term).

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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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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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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 value of the secondary industry, population, terminal power consumption intensity, and per capita disposable income. Subsequently, eight combined forecasting models based on the grey model (GM), multiple linear regression (MLR), and error back propagation neural network (BP) are constructed to predict and analyse the electricity consumption of the whole society in Xinjiang. The results indicate the optimal weighted combination forecasting model, GM-MLR-BP of the induced ordered weighted harmonic averaging operator (IOWHA operator), exhibits better prediction accuracy, and the effectiveness of the proposed method is proven.

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Wu, Tan, De, Pu, Wang, Tan, and Ju. "Multiple Scenarios Forecast of Electric Power Substitution Potential in China: From Perspective of Green and Sustainable Development." Processes 7, no. 9 (September 2, 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 well and important influencing factors are selected and transmitted to the CSO-ELM model to carry out the fitting analysis. The results showed that the CSO-ELM model has great forecasting accuracy. Finally, combining with the cost, policy supports, subsidy mechanism and China’s power consumption data in the past 21 years, four forecasting scenarios are designed and the forecasting results of 2019–2030 are calculated, respectively. Results under multiple scenarios may give suggestions for future sustainable development.

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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 Component Method with the assessment of the impact of production factors on electrical power consumption using elasticity coefficients, as well as the energy saving factor based on a variable structure model; trend additive and multiplicative forecast models of electrical consumption that take into account the seasonality factor, models with a change in trends, a linear dynamic model of electrical power consumption that takes into account the production output; a forecast adaptive polynomial model of electrical power consumption as well as the Winters model. The developed forecast models have a sufficiently high accuracy (accuracy of the MAPE was below 7%). The choice of the model type to forecast the electrical power consumption depends on the quantitative and qualitative characteristics of the time series, the structural relation between the series, the purpose and objectives of the modeling. In order to enhance the accuracy of the forecast it is required to regularly refine the model and adjust it to the actual situation with the due account of new factors and production trends while building different versions of scenarios and combined forecast models of electrical power consumption

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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 (July 3, 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 tweets is to reflect human activity dynamics to some degree by considering human beings as sensors, which therefore can be employed at the building level. High correlation is found between the human activity indicator and the power consumption as supported by a correlation coefficient level over 0.8. To the best of our knowledge, rare studies placed an emphasis on hourly electric power consumption using social media data, especially at such a fine scale. This research shows the great potential of using Twitter data as a proxy of human activities to model hourly electric power consumption at the building level. More studies are warranted in the future to further examine the effectiveness of the proposed method in this research.

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Dissertations / Theses on the topic "Electric power consumption Victoria Forecasting"

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

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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 model has seven parameters, each with a natural interpretation (one parameter representing minimum demand in a day, two parameters representing the time of morning and afternoon peaks, two parameters representing the shape of each peak, and two parameters representing the total energy per peak). With the help of this model, we trace change in the demand profile over a number of years. The proposed model will be helpful for short to long term electricity demand forecasting.

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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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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 statistical techniques. Although weather and economic effects have significant influences on electricity demand, in this study they are purposely eliminated from investigation. This research considers calendar-related effects such as months of the year, weekdays and holidays (that is, public holidays, the day before a public holiday, the day after a public holiday, school holidays, university holidays, Easter holidays and major religious holidays) and includes university exams, general election days, day after elections, and municipal elections in the analysis. Regression analysis, cate-gorical regression and auto-regression are used to illustrate the relationships between response variable and explanatory variables. The main objective of the investigation was to build forecasting models based on this calendar data only and to observe how accurate the models can be without taking into account weather effects and economic effects, hence weather neutral models. Weather and economic factors have to be forecasted, and these forecasts are not so accurate and calendar events are known for sure (error-free). Collecting data for weather and economic factors is costly and time consuming, while obtaining calendar data is relatively easy.

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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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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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Thesis (M.S.)--West Virginia University, 1999.
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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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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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 system consists of a network of computers, data and graphics terminals, controllers, modems and other communication hardware, and the necessary software. The network of interactive computers divides the responsibility of monitoring of meteorological data, electric load, and performing other functions. These functions include: data collection, processing and archiving, load forecasting, load modeling, information display and alarm processing. Each of these functions requires a certain amount of intelligence depending on the sophistication and complication of that function. Also, a high level of reliability has been provided to each function to guarantee an uninterrupted operation of the system. A full scale simulation of this concept was carried out in the laboratory using five microcomputers and the necessary communication hardware. An important and integral part of the research effort is the development of the short-term load forecast, load models and the decision support system using rule-based algorithms and expert systems. Each of these functions has shown the ability to produce more accurate results compared to classical techniques while at the same time requiring much less computing time and historical data. Development of these functions has made the use of microcomputers for constructing an integrated load management system possible and practical. Also, these functions can be applied for other applications in the electric utility industry and maintain their importance and contribution. In addition to that, the use of rule-based algorithms and expert systems promises to yield significant benefits in using microcomputers in the load management area.
Ph. D.

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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 strengths and weaknesses in this process by illuminating its technical, organizational and personal dimensions. Examination of the forecast from the technical perspective revealed an elaborate set of interlinked models used to develop baseline, high, and low forecasts. The organizational perspective revealed BPA to be in a transitional stage. Internally, ratemaking, forecasting, conservation, resource acquisition, and financial management swelled as new organizational functions. Interorganizationally, environmentalists, ratepayer groups, and the region's utilities all had strong interests in the decision regarding WPPSS plants. The personal perspective revealed that each of the Administrators heading BPA since the early 1980s defined the agency's approach to the resource planning problem differently, first as an engineering problem, then as a political problem, and, finally, as a business problem. Taken together, the Multiple Perspectives yielded the following conclusions about BPA's 1982 forecast. (1) BPA's range forecast constituted a major improvement over the point forecasts preceding it, but left important classes of uncertainty unexplored. (2) BPA's models were better suited to address rate and conservation issues important at the time of the 1982 forecast than their predecessors. The model of the national economy, however, remained a black box, potentially significant feedbacks were not represented, and the sheer size of the modeling system placed practical limits on its use. (3) A stronger method of dealing with forecast uncertainty is needed which utilizes a disaster-avoidance strategy and plans for high impact/low probability events. This method need not involve the use of large models, but should incorporate qualitative insights from persons normally outside the technical sphere.

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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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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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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
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. This project developed a model for short term load forecasting using a neural network. The concept was tested by evaluating the forecasting potential of the basic feedforward and the cascade forward neural network models. The test results showed that the cascade forward model is more efficient for this forecasting investigation. The final model is intended to be a basis for a real forecasting application. The neural model was tested using actual load data of the Bloemfontein reticulation network to predict its load for half an hour in advance. The cascade forward network demonstrates a mean absolute percentage error of less than 5% when tested using four years of utility data. In addition to reporting the summary statistics of the mean absolute percentage error, an alternate method using correlation coefficients for presenting load forecasting performance results are shown. This research proposes that a 6:1:1 cascade forward neural network can be trained with data from a month of a year and forecast the load for the same month of the following year. This research presents a new time series modeling for short term load forecasting, which can model the forecast of the half-hourly loads of weekdays, as well as of weekends and public holidays. Obtained results from extensive testing on the Bloemfontein power system network confirm the validity of the developed forecasting approach. This model can be implemented for on-line testing application to adopt a final view of its usefulness.

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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 that are solved by least squares regression using temperature data and timeseries analysis. The results indicate that each consumer consumption segment is highly dependent of the temperature with R2-values of above 90%. However, modelling each segment completely by time-series analysis proved to be more efficient due to lack of flexibility in the polynomials, lack of used weather stations and seasonal patterns in addition to the temperatures. Although the forecasting models could not beat analysts’ consensus estimates, these present natural gas storage level drivers and can thus be used to incorporate temperature forecasts when estimating futures prices.

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Books on the topic "Electric power consumption Victoria Forecasting"

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

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Spatial electric load forecasting. New York: Marcel Dekker, 1996.

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Estomin, Steven. Forecasted electric power demands for the Potomac Electric Power Company. [Annapolis, Md.]: The Program, 1988.

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

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W, Gellings Clark, and Barron W. L, eds. Demand forecasting in the electric utility industry. 2nd ed. Tulsa, OK: PennWell Pub., 1996.

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Zhongguo dian li xu qiu zhan wang: Ji yu dian li gong xu yan jiu shi yan shi mo ni shi yan (2010). Beijing: Zhongguo dian li chu ban she, 2010.

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

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

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

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Yépez-Garcia, Rigoberto Ariel. Meeting the balance of electricity supply and demand in Latin America and the Caribbean. Washington, D.C: World Bank, 2011.

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Book chapters on the topic "Electric power consumption Victoria Forecasting"

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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, 210–15. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01328-8_24.

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Panchal, R., and B. Kumar. "Forecasting industrial electric power consumption using regression based predictive model." In Recent Trends in Communication and Electronics, 135–39. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003193838-26.

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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, 43–70. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003293989-3.

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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, 1521–30. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11058-0_154.

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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, 91–102. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_8.

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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 research handles the mileage of electric vehicles, the article illustrates how to find simple yet robust and precise models to predict power consumption and range by using basic parameters from transport planning only. In the paper, we argue that the precision of range and consumption estimates can be substantially improved compared to common approaches which usually posit a proportional relationship between energy consumption and travel distance and require substantial safety buffers.

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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 electric power demand forecasting, and presents the different models. This paper proposes a novel approach using machine learning for electric power demand forecasting.

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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 certain period. The first objective of this research is to propose DSARIMA to predict electric load. Furthermore, the results of the load prediction are used as electrical load clustering data through a descriptive analytical approach. The best model DSARIMA forecasting is ([1, 2, 5, 6, 7, 11, 16, 18, 35, 46], 1, [1, 3, 13, 21, 27, 46]) (1, 1, 1)48 (0, 0, 1)336 with a MAPE of 1.56 percent. The cluster pattern consists of four groups with a range of intervals between the minimum and maximum data values divided by the quartile. The presentation of this research data is based on data on the consumption of electricity loads every half hour at the Generating Unit, the National Electricity Company in Gresik City, Indonesia.

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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, 206–18. 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 results of the best-fitted machine learning (ML) model with a deep learning model, long short-term memory based on RNN (LSTM-RNN). RNN model has vanishing gradient issue, which slows down the training in the early layers of the network. LSTM-RNN is the advanced model which take care of vanishing gradient problem. The performance evaluation metric to compare the superiority of the model will be R2, mean square error (MSE), root means square error (RMSE), and mean absolute error (MAE).

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Conference papers on the topic "Electric power consumption Victoria Forecasting"

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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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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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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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BARBULESCU, Constantin. "ANN BASED MONTHLY POWER CONSUMPTION FORECASTING. CASE STUDY FOR A ROMANIAN ELECTRIC ENERGY DISTRIBUTION OPERATOR." In 18th International Multidisciplinary Scientific GeoConference SGEM2018. STEF92 Technology, 2018. http://dx.doi.org/10.5593/sgem2018v/4.3/s11.054.

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Shaad, M., A. Momeni, C. P. Diduch, M. E. Kaye, and L. Chang. "Forecasting the power consumption of a single domestic electric water heater for a direct load control program." In 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2015. http://dx.doi.org/10.1109/ccece.2015.7129511.

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Kivchun, O. R. "FORECASTING ELECTRIC POWER CONSUMPTION OF TECHNOCENOSIS OBJECTS ON THE BASIS OF VALUES FROM TRANSFORMED VECTOR RANK DISTRIBUTION." In Mechanical Science and Technology Update. Omsk State Technical University, 2021. http://dx.doi.org/10.25206/978-5-8149-3246-4-2021-178-182.

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В докладе представлен метод прогнозирования электропотребления на основе векторного рангового анализа. Отличительной особенностью векторного рангового анализа является возможность представления рангового параметрического распределения в векторном ранговом пространстве, которое характеризуется достаточным набором показателей. На основе показателей векторного рангового анализа существует возможность исследовать как статическое состояние электросетевого комплекса, так и динамическое. Одним из них является ранговый фазовый угол. Полученное значение рангового фазового угла используется для прогнозирования. Экспериментальная проверка метода показала, что показатели прогнозирования электропотребления на основе векторного рангового анализа значительно лучше, т.е. точность прогноза выше. В связи с этим, для реализации среднесрочного прогнозирования электропотребления на объектах РЭСК целесообразно выбрать метод на основе векторного рангового анализа

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