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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, 2012Because 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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Grando, Neusa. "Máquina de estado líquido para previsão de séries temporais contínuas: aplicação na demanda de energia elétrica." Universidade Tecnológica Federal do Paraná, 2010. http://repositorio.utfpr.edu.br/jspui/handle/1/896.
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CAPESUm dos aspectos fundamentais da inteligência natural é sua aptidão no processamento de informações temporais. O grande desafio proposto é o de desenvolver sistemas inteligentes que mapeiem essa aptidão do comportamento humano. Neste contexto, aportam as Máquinas de Estado Líquido (LSMs), uma arquitetura neural pulsada (meio líquido) que projeta os dados de entrada em um espaço dinâmico de alta dimensão e, por conseguinte, realiza a análise do conjunto de dados de entrada através de uma rede neural clássica (unidade de leitura). Desta maneira, esta tese apresenta uma solução inovadora para a previsão de séries temporais contínuas através das LSMs com mecanismo de reinicialização e entradas analógicas, contemplando a área da demanda de energia elétrica. A metodologia desenvolvida foi aplicada no horizonte de previsão a curto prazo e a longo prazo. Os resultados obtidos são promissores, considerando o alto erro estabelecido para parada do treinamento da unidade de leitura, o baixo número de iterações do treinamento da unidade de leitura e que nenhuma estratégia de ajustamento sazonal, ou pré-processamento, sob os dados de entrada foi realizado. Até o momento, percebe-se que as LSMs têm despontado como uma nova e promissora abordagem dentro do paradigma conexionista, emergente da ciência cognitiva.
Among of several aspects of the natural intelligence is its ability to process temporal information. One of major challenges to be addresses is how to efficiently develop intelligent systems that integrate the complexities of human behavior. In this context, appear the Liquid State Machines (LSMs), a pulsed neural architecture (liquid) that projects the input data in a high-dimensional dynamical space and therefore makes the analysis of input data all through a classical neural network (readout). Thus, this thesis presents an innovative solution for forecasting continuous time series through LSMs with reset mechanism and analog inputs, applied to the electric energy demand. The methodology was applied in the short-term and long-term forecasting of electrical energy demand. Results are promising, considering the high error to stop training the readout, the low number of iterations of training of the readout, and that no strategy of seasonal adjustment or preprocessing of input data was achieved. So far, it can be notice that the LSMs have been studied as a new and promising approach in the Artificial Neural Networks paradigm, emergent from cognitive science.
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Talmo, Dan. "Energy use and forecasting in Wisconsin manufacturing industries." 1987. http://catalog.hathitrust.org/api/volumes/oclc/16319156.html.
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Walton, Alison Norma. "Forecasting the monthly electricity consumption of municipalities in KwaZulu-Natal." Thesis, 1997. http://hdl.handle.net/10413/5743.
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Eskom is the major electricity supplier in South Africa and medium term forecasting within the company is a critical activity to ensure that enough electricity is generated to support the country's growth, that the networks can supply the electricity and that the revenue derived from electricity consumption is managed efficiently. This study investigates the most suitable forecasting technique for predicting monthly electricity consumption, one year ahead for four major municipalities within Kwa-Zulu Natal.Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 1997.
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Ravele, Thakhani. "Medium term load forecasting in South Africa using Generalized Additive models with tensor product interactions." Diss., 2018. http://hdl.handle.net/11602/1165.
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MSc (Statistics)Department of Statistics
Forecasting of electricity peak demand levels is important for decision makers in Eskom. The overall objective of this study was to develop medium term load forecasting models which will help decision makers in Eskom for planning of the operations of the utility company. The frequency table of hourly daily demands was carried out and the results show that most peak loads occur at hours 19:00 and 20:00, over the period 2009 to 2013. The study used generalised additive models with and without tensor product interactions to forecast electricity demand at 19:00 and 20:00 including daily peak electricity demand. Least absolute shrinkage and selection operator (Lasso) and Lasso via hierarchical interactions were used for variable selection to increase the model interpretability by eliminating irrelevant variables that are not associated with the response variable, this way also over tting is reduced. The parameters of the developed models were estimated using restricted maximum likelihood and penalized regression. The best models were selected based on smallest values of the Akaike information criterion (AIC), Bayesian information criterion (BIC) and Generalized cross validation (GCV) along with the highest Adjusted R2. Forecasts from best models with and without tensor product interactions were evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE). Operational forecasting was proposed to forecast the demand at hour 19:00 with unknown predictor variables. Empirical results from this study show that modelling hours individually during the peak period results in more accurate peak forecasts compared to forecasting daily peak electricity demand. The performance of the proposed models for hour 19:00 were compared and the generalized additive model with tensor product interactions was found to be the best tting model.
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Thanyani, Maduvhahafani. "Forecasting hourly electricity demand in South Africa using machine learning models." Diss., 2020. http://hdl.handle.net/11602/1595.
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MSc (Statistics)Department of Statistics
Short-term load forecasting in South Africa using machine learning and statistical models is discussed in this study. The research is focused on carrying out a comparative analysis in forecasting hourly electricity demand. This study was carried out using South Africa’s aggregated hourly load data from Eskom. The comparison is carried out in this study using support vector regression (SVR), stochastic gradient boosting (SGB), artificial neural networks (NN) with generalized additive model (GAM) as a benchmark model in forecasting hourly electricity demand. In both modelling frameworks, variable selection is done using least absolute shrinkage and selection operator (Lasso). The SGB model yielded the least root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) on testing data. SGB model also yielded the least RMSE, MAE and MAPE on training data. Forecast combination of the models’ forecasts is done using convex combination and quantile regres- sion averaging (QRA). The QRA was found to be the best forecast combination model ibased on the RMSE, MAE and MAPE.
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Payne, Daniel Frederik. "The forecasting of transmission network loads." Diss., 1997. http://hdl.handle.net/10500/15752.
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The forecasting of Eskom transmission electrical network demands is a complex task. The lack of historical data on some of the network components complicates this task even further. In this dissertation a model is suggested
which will address all the requirements of the transmission system expansion engineers in terms of future loads and market trends. Suggestions are made with respect to ways of overcoming the lack of historical data, especially on the point loads, which is a key factor in modelling the electrical networks. A brief overview of the transmission electrical network layout is included to provide a better understanding of what is required from such a forecast. Lastly, some theory on multiple regression, neural networks and qualitative forecasting techniques is included, which will be of value for further model developments.Computing
M. Sc. (Operations Research)
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Alani, Adeshina Yahaha. "Short-term multiple forecasting of electric energy loads with weather profiles for sustainable demand planning in smart grids for smart homes." Diss., 2018. http://hdl.handle.net/10500/25216.
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Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Therefore, effective prediction of future electricity consumption cannot be underestimated. Notably, repeated imbalance is noticed between the demand and supply of electricity, and this is affected by different weather profiles such as temperature, wind speed, dew point, humidity and pressure of the electricity consumption locations. Effective planning is therefore needed to aid electricity distribution among consumers. Such effective planning is activated by the need to predict future electricity consumption within a short period and the effect of weather variables on the predictions. Although state-of-the-art techniques have been used for such predictions, they still require improvement for the purpose of reducing significant predictive errors when used for short-term load forecasting. This research develops and deploys a near-zero cooperative probabilistic scenario analysis and decision tree (PSA-DT) model to address the lacuna of significant predictive error faced by the state-of-the-art models and to analyse the effect of each weather profile on the cooperative model. The PSA-DT is a machine learning model based on a probabilistic technique in view of the uncertain nature of electricity consumption, complemented by a DT to reinforce the collaboration of the two techniques. Based on detailed experimental analytics on residential, commercial and industrial data loads, the PSA-DT model with weather profiles outperforms the state-of-the-art models in terms of accuracy to a minimal error rate. This implies that its deployment for electricity demand planning will be of great benefit to various smart-grid operators and homes.School of Computing
M. Sc. (Computer Science)