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

Author: Grafiati

Published: 10 December 2022

Last updated: 31 July 2025

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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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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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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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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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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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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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11

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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CAPES<br>Um 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

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12

Talmo, Dan. "Energy use and forecasting in Wisconsin manufacturing industries." 1987. http://catalog.hathitrust.org/api/volumes/oclc/16319156.html.

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13

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.<br>Thesis (M.Sc.)-University of Natal, Pietermaritzburg, 1997.

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14

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)<br>Department of Statistics<br>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 electrici

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15

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)<br>Department of Statistics<br>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 electrici

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16

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 in

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17

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 locati

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