High electricity prices have become the title of several news articles recently in Sweden and the prices have experienced large sudden fluctuations during certain periods. In this thesis work, a smart control model for the electricity usage in three different households has been developed with the main purpose to minimize the electricity cost. This has been implemented by using mixed-integer linear programming (MILP) to optimize the cost 24 hours ahead, and by forecasting two of the main inputs; the load and the electricity spot prices for bidding zone three (SE3) in Sweden. The units included in the model are the photovoltaic system, the batteries, the electricity consumption in the house and the electric vehicles. However, the main task of the smart control was to determine when and in which amount the energy should flow from one unit to another, or to/from the grid. In other words, it decides the charging/discharging of the batteries, the selling/buying of electricity and the charging of the electric vehicle (EV). Different amounts of cost savings/profits have been obtained when applying the smart control on the three houses, which have different annual consumption, capacities of the components, heating systems and more. The results showed that it is most optimal to run the model between the time interval 13.00-00.00, when the spot prices for the next day are known, in order to avoid the remarkable impact accompanied with the use of forecasted electricity prices as input to the model. The forecasting of the load is, on the other hand, required to run the model, but this thesis showed that the effect of the uncertainties in this forecast is relatively small. Three types of machine learning methods were implemented to perform the forecasts, namely linear regression (LR), decision tree regression and random forest regression. After measuring especially the mean absolute error (MAE) to validate the results, the random forest regression showed the least error and the other methods showed close results when looking at the electric load prognosis.

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.

Submitted by Igoor Morro Mello (igoor.mello@unesp.br) on 2018-09-03T21:44:57Z No. of bitstreams: 1 dissertação de mestrado 2018 - Igoor Morro Mello.pdf: 10593338 bytes, checksum: 0253d1ce16d1ea2f863390cd1d68fd1e (MD5) / Approved for entry into archive by Cristina Alexandra de Godoy null (cristina@adm.feis.unesp.br) on 2018-09-04T17:09:34Z (GMT) No. of bitstreams: 1 mello_im_me_ilha.pdf: 10593338 bytes, checksum: 0253d1ce16d1ea2f863390cd1d68fd1e (MD5) / Made available in DSpace on 2018-09-04T17:09:34Z (GMT). No. of bitstreams: 1 mello_im_me_ilha.pdf: 10593338 bytes, checksum: 0253d1ce16d1ea2f863390cd1d68fd1e (MD5) Previous issue date: 2018-08-24 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Nos últimos anos, o uso dos veículos elétricos nas zonas urbanas tem se intensificado. Como política para o aumento na penetração de veículos elétricos e reduzir a poluição do ar, os táxis elétricos vem sendo introduzidos nos sistemas de transporte. Eles necessitam de atenção especial devido aos seus diferentes padrões de condução. Em contraste com os veículos elétricos privados, que podem ser recarregados por um longo período, táxis elétricos necessitam de recarga em um curto período de tempo devido a sua constante operação. Portanto, estações de recarga rápida são necessárias para receber a demanda de recarga dos táxis elétricos e devem estar localizadas em locais estratégicos. Além disso, uma análise deve ser realizada para a conexão destas estações com a rede elétrica. Para melhorar sua alocação e conectividade, este trabalho apresenta uma metodologia para auxiliar na tomada de decisão da instalação de estações de recarga rápida considerando como critérios: locais com maior fluxo de táxis elétricos e baixo nível de carga nas baterias, espaço físico disponível para realizar o carregamento e funções de custo para a conexão das estações de recarga. Os resultados da proposta são mapas com a localização das estações de recarga rápida e análise dos locais de menor custo para a conexão com a rede elétrica. A metodologia é testada em uma cidade de médio porte no Brasil, mostrando a importância dos mapas e funções de custo na tomada de decisão. A proposta é comparada com outras metodologias, mostrando que esta metodologia proposta considera diferentes critérios e cria uma melhor distribuição espacial para as estações de recarga, dando melhores opções aos donos dos táxis elétricos. / In recent years, the use of electric vehicles in urban zones has been intensified. As a policy of increasing the penetration of electric vehicles and reducing air pollution, electric taxis have been introduced into transportation systems. They need special attention because of its different driving patterns. In contrast to private electric vehicles, which can be recharged for a long period, electric taxis need to recharge only for a short time due to their constant operation. Therefore, fast charging stations are required to meet the demand for recharging electric taxis and should be located at strategic places. In addition, an analysis must be performed to connect these stations in the electric network. To improve their allocation and connectivity, this work presents a methodology to help in decision making for installing fast charging stations, considering as criteria: locations with greater flow of electric taxis and low level of state of charge, the available physical space to carry out their recharge and cost functions for the connection of charging stations. The result of the proposal is a map with the location of fast charging stations and analysis of the lowest cost places for connection to the network. The methodology is tested in a medium-sized city in Brazil, showing the importance of this map and cost functions in decision making. The proposal is compared with another methodology, showing that the proposed method considers different criteria and creates a better spatial distribution of charging stations giving better options to the owners of electric taxis. / CAPES: 1667419

Orientador: Antonio Padilha Feltrin / Abstract: In recent years, the use of electric vehicles in urban zones has been intensified. As a policy of increasing the penetration of electric vehicles and reducing air pollution, electric taxis have been introduced into transportation systems. They need special attention because of its different driving patterns. In contrast to private electric vehicles, which can be recharged for a long period, electric taxis need to recharge only for a short time due to their constant operation. Therefore, fast charging stations are required to meet the demand for recharging electric taxis and should be located at strategic places. In addition, an analysis must be performed to connect these stations in the electric network. To improve their allocation and connectivity, this work presents a methodology to help in decision making for installing fast charging stations, considering as criteria: locations with greater flow of electric taxis and low level of state of charge, the available physical space to carry out their recharge and cost functions for the connection of charging stations. The result of the proposal is a map with the location of fast charging stations and analysis of the lowest cost places for connection to the network. The methodology is tested in a medium-sized city in Brazil, showing the importance of this map and cost functions in decision making. The proposal is compared with another methodology, showing that the proposed method considers different criteria and creates a better s... (Complete abstract click electronic access below) / Resumo: Nos últimos anos, o uso dos veículos elétricos nas zonas urbanas tem se intensificado. Como política para o aumento na penetração de veículos elétricos e reduzir a poluição do ar, os táxis elétricos vem sendo introduzidos nos sistemas de transporte. Eles necessitam de atenção especial devido aos seus diferentes padrões de condução. Em contraste com os veículos elétricos privados, que podem ser recarregados por um longo período, táxis elétricos necessitam de recarga em um curto período de tempo devido a sua constante operação. Portanto, estações de recarga rápida são necessárias para receber a demanda de recarga dos táxis elétricos e devem estar localizadas em locais estratégicos. Além disso, uma análise deve ser realizada para a conexão destas estações com a rede elétrica. Para melhorar sua alocação e conectividade, este trabalho apresenta uma metodologia para auxiliar na tomada de decisão da instalação de estações de recarga rápida considerando como critérios: locais com maior fluxo de táxis elétricos e baixo nível de carga nas baterias, espaço físico disponível para realizar o carregamento e funções de custo para a conexão das estações de recarga. Os resultados da proposta são mapas com a localização das estações de recarga rápida e análise dos locais de menor custo para a conexão com a rede elétrica. A metodologia é testada em uma cidade de médio porte no Brasil, mostrando a importância dos mapas e funções de custo na tomada de decisão. A proposta é comparada com outras me... (Resumo completo, clicar acesso eletrônico abaixo) / Mestre

Orientador: Takaaki Ohishi / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-08T10:06:35Z (GMT). No. of bitstreams: 1 LeoneFilho_MarcosdeAlmeida_M.pdf: 1557959 bytes, checksum: 92dc63d4e3140cc61ba7900961c0e9fb (MD5) Previous issue date: 2006 / Resumo: Neste trabalho é proposta uma metodologia para previsão de séries temporais de carga de energia elétrica de curto prazo. Esta metodologia vem sendo muito utilizada no contexto da previsão de séries temporais e do reconhecimento de padrões. Os autores que propuseram esta metodologia a chamaram de "Ensembles". Este nome tenta explicar o é este modelo: uma combinação de partes que juntas formam um só modelo. Neste sentido, este nome expressa com relativa clareza qual é o principal aspecto desta metodologia, que no caso específico deste trabalho, é o de fazer várias previsões de uma mesma série temporal utilizando diferentes ferramentas que sozinhas são suficientemente competentes para prever a série temporal em questão, e em seguida combinar as soluções para, deste modo, tentar obter uma solução melhor do que quando é usada somente uma ferramenta. As ferramentas usadas para compor a previsão dos "Ensembles" finais são Redes Neurais Artificiais (RNAs) e Redes Neurais Nebulosas. Atualmente, estas redes são largamente utilizadas em problemas de previsão de séries temporais, principalmente quando o fator gerador destas séries é um sistema não-linear. Desta forma, isto as tornou candidatas potenciais para prever valores de uma série de cargas de energia elétrica, pois este tipo de série tem características essencialmente não-lineares. Sendo assim, foram utilizados quatro tipos de redes: RNAs MLPs, RNAs Recorrentes, RNAs de Base Radial e Redes Neurais Nebulosas tipo ANFIS. Com os modelos básicos de redes foram, utilizados Algoritmos Genéticos para evoluir os parâmetros destas redes e, assim, chegar a uma população de redes suficientemente competentes para fazer as previsões da série de cargas. Na próxima etapa, com os resultados das previsões da população de redes evoluídas foi feita a seleção dos melhores agrupamentos destas redes evoluídas e, como este processo requer a avaliação de diferentes configurações de modelos, esta seleção é baseada em Algoritmos Genéticos.Os resultados obtidos ao se utilizar "ensembles" mostraram que este modelo foi capaz de alcançar uma grande robustez na previsão, reduzindo os erros de previsão, suavizando os resultados de previsão e deixando o modelo menos suscetível a grandes erros quando surgem "outliers" no conjunto de dados / Abstract: This work proposes a methodology for short-term electric power load forecasting. This methodology is being widely used under the context of time series prediction and pattern recognition. It was named "ensembles" by the authors who developed it. This name carries the meaning of an assemblage of parts considered as forming a whole. Therefore, this name expresses rather clearly the main characteristic of this methodology, which under the framework of this study is to make several predictions of the same time series using various different tools in which every single one alone is sufficiently competent to predict the above mentioned time series. After that, the predictions are combined in order to achieve a better prediction compared to the one that is obtained if a single predictor is used. The tools implemented to form the final "ensembles" prediction are Artificial Neural Networks (ANNs) and Neuro-fuzzy Networks. Nowadays, these networks are being widely used in time series predictions problems, mainly when the factor that generates these series is a non-linear system. Hence, this fact has elected them as potential candidates to predict future values of an electric power load series because this series has essentially non-linear characteristics. As a result, four types of networks were utilized in this work: MLPs ANNs, Recurrent ANNs, Radial Basis ANNs and ANFIS type Neuro-fuzzy networks. So, with the basic networks models, Genetic Algorithms were applied to evolve the parameters of these networks and, as a consequence, a population of networks sufficiently capable of predicting future values of the load time series was built. On the next step, with the results obtained from the evolved population of networks, a selection of the most suitable results of the individual networks were made and, as soon as this process implies the evaluation of multiple different combinations of models, this methodology was based on Genetic Algorithms. Then, this selected networks were combined. The results when using "ensembles" revealed that this model was able to reach a great robustness in prediction tasks. In that sense, it was possible to reduce the level of prediction error, to smooth the resulting predictions and to make the model more stable reducing the possibilities of presenting high levels of errors when the used data set contains "outliers" / Mestrado / Energia Eletrica / Mestre em Engenharia Elétrica

How much electricity is going to be consumed for the next 24 hours? What will be the temperature for the next three days? What will be the number of sales of a certain product for the next few months? Answering these questions often requires forecasting several future observations from a given sequence of historical observations, called a time series. <p><p>Historically, time series forecasting has been mainly studied in econometrics and statistics. In the last two decades, machine learning, a field that is concerned with the development of algorithms that can automatically learn from data, has become one of the most active areas of predictive modeling research. This success is largely due to the superior performance of machine learning prediction algorithms in many different applications as diverse as natural language processing, speech recognition and spam detection. However, there has been very little research at the intersection of time series forecasting and machine learning.<p><p>The goal of this dissertation is to narrow this gap by addressing the problem of multi-step-ahead time series forecasting from the perspective of machine learning. To that end, we propose a series of forecasting strategies based on machine learning algorithms.<p><p>Multi-step-ahead forecasts can be produced recursively by iterating a one-step-ahead model, or directly using a specific model for each horizon. As a first contribution, we conduct an in-depth study to compare recursive and direct forecasts generated with different learning algorithms for different data generating processes. More precisely, we decompose the multi-step mean squared forecast errors into the bias and variance components, and analyze their behavior over the forecast horizon for different time series lengths. The results and observations made in this study then guide us for the development of new forecasting strategies.<p><p>In particular, we find that choosing between recursive and direct forecasts is not an easy task since it involves a trade-off between bias and estimation variance that depends on many interacting factors, including the learning model, the underlying data generating process, the time series length and the forecast horizon. As a second contribution, we develop multi-stage forecasting strategies that do not treat the recursive and direct strategies as competitors, but seek to combine their best properties. More precisely, the multi-stage strategies generate recursive linear forecasts, and then adjust these forecasts by modeling the multi-step forecast residuals with direct nonlinear models at each horizon, called rectification models. We propose a first multi-stage strategy, that we called the rectify strategy, which estimates the rectification models using the nearest neighbors model. However, because recursive linear forecasts often need small adjustments with real-world time series, we also consider a second multi-stage strategy, called the boost strategy, that estimates the rectification models using gradient boosting algorithms that use so-called weak learners.<p><p>Generating multi-step forecasts using a different model at each horizon provides a large modeling flexibility. However, selecting these models independently can lead to irregularities in the forecasts that can contribute to increase the forecast variance. The problem is exacerbated with nonlinear machine learning models estimated from short time series. To address this issue, and as a third contribution, we introduce and analyze multi-horizon forecasting strategies that exploit the information contained in other horizons when learning the model for each horizon. In particular, to select the lag order and the hyperparameters of each model, multi-horizon strategies minimize forecast errors over multiple horizons rather than just the horizon of interest.<p><p>We compare all the proposed strategies with both the recursive and direct strategies. We first apply a bias and variance study, then we evaluate the different strategies using real-world time series from two past forecasting competitions. For the rectify strategy, in addition to avoiding the choice between recursive and direct forecasts, the results demonstrate that it has better, or at least has close performance to, the best of the recursive and direct forecasts in different settings. For the multi-horizon strategies, the results emphasize the decrease in variance compared to single-horizon strategies, especially with linear or weakly nonlinear data generating processes. Overall, we found that the accuracy of multi-step-ahead forecasts based on machine learning algorithms can be significantly improved if an appropriate forecasting strategy is used to select the model parameters and to generate the forecasts.<p><p>Lastly, as a fourth contribution, we have participated in the Load Forecasting track of the Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem where we were required to backcast and forecast hourly loads for a US utility with twenty geographical zones. Our team, TinTin, ranked fifth out of 105 participating teams, and we have been awarded an IEEE Power & Energy Society award.<p> / Doctorat en sciences, Spécialisation Informatique / info:eu-repo/semantics/nonPublished

MSc (Statistics) / Department of Statistics / Generally, short term load forecasting is essential for any power generating utility. In this dissertation the main objective was to develop short term load forecasting models for the peak demand periods (i.e. from 18:00 to 20:00 hours) in South Africa using. Quantile semi-parametric additive models were proposed and used to forecast electricity demand during peak hours. In addition to this, forecasts obtained were then used to nd an optimal number of generating units to commit (switch on or o ) daily in order to produce the required electricity demand at minimal costs. A mixed integer linear programming technique was used to nd an optimal number of units to commit. Driving factors such as calendar e ects, temperature, etc. were used as predictors in building these models. Variable selection was done using the least absolute shrinkage and selection operator (Lasso). A feasible solution to the unit commitment problem will help utilities meet the demand at minimal costs. This information will be helpful to South Africa's national power utility, Eskom. / NRF

[pt] No Brasil, devido aos incentivos governamentais ministrados na área de energia renovável, é postulada uma perspectiva crescente no número de instalações de micro e minigeração distribuída (MMGD), sendo a fonte solar destaque no país. Dessa forma, o aumento na inserção de fontes intermitentes promove alterações significativas no comportamento da curva de carga horária, podendo atingir de maneira direta a operação e o planejamento da rede elétrica. Para atender aos novos panoramas dispostos pelo sistema elétrico brasileiro, esta dissertação propõe uma nova metodologia para contabilizar a geração distribuída fotovoltaica para as horas que compõem o dia. Usando o modelo Holt-Winters Sazonal Duplo são feitas previsões de carga e demanda para o Sistema Interligado Nacional e os subsistemas que o integram, considerando, em particular, o impacto causado pela conexão destes sistemas de MMGD solar fotovoltaica na rede de distribuição. Para as previsões são utilizados o horizonte de tempo de 24 horas, em intervalos horários, efetuadas para a primeira semana de 2020. Os resultados indicam que a metodologia proposta para a criação das séries de geração distribuída fotovoltaica é válida, pois é observada uma diminuição dos erros de previsão para a série de demanda, constituída pelo montante da geração distribuída adicionado a carga. Os valores de MAPE analisados neste trabalho não ultrapassam 10 porcento para dias típicos, exceto feriados, indicando que o método apresentado é um recurso eficiente. / [en] In Brazil, due to government incentives given in the area of renewable energy, a growing perspective in the number of micro and mini distributed generation (MMGD) installations is postulated, being the solar source highlighted in the country. Thus, the increase in the insertion of intermittent sources promotes significant changes in the behavior of the hourly load curve, which can directly affect the operation and planning of the electrical network. To meet the new panoramas provided by the Brazilian electricity system, this dissertation proposes a new methodology to account for distributed photovoltaic generation for the hours that make up the day. Using the Double Seasonal Holt-Winters model, load and demand forecasts are made for the National Interconnected System and the subsystems that integrate it, considering, in particular, the impact caused by the connection of these solar photovoltaic MMGD systems in the distribution network. For the forecasts, the 24-hour time horizon is used, in hourly intervals, carried out for the first week of 2020. The results indicate that the proposed methodology for the creation of distributed photovoltaic generation series is valid, as it is observed a decrease in the forecast errors for the demand series, constituted by the amount of the distributed generation added to the load. The MAPE values analyzed in this work do not exceed 10 percent for typical days, except holidays, indicating that the presented method is an efficient resource.

Sports events, festivals, construction sites, and film sites are examples of cases where power is required temporarily and often away from the power grid. Temporary Power Installations refer to systems set up for a limited amount of time with power typically generated on-site. Most load forecasting research has centered around settings with a permanent supply of power (such as in residential buildings). On the contrary, this work proposes machine learning approaches to accurately forecast load for Temporary Power Installations. In practice, these systems are typically powered by diesel generators that are over-sized and consequently, operate at low inefficient load levels. In this thesis, a ‘Pre-Event Forecasting’ approach is proposed to address this inefficiency by classifying a new Temporary Power Installation to a cluster of installations with similar load patterns. By doing so, the sizing of generators and power generation planning can be optimized thereby improving system efficiency. Load forecasting for Temporary Power Installations is also useful whilst a Temporary Power Installation is operational. A ‘Real-Time Forecasting’ approach is proposed to use monitored load data streamed to a server to forecast load two hours or more ahead in time. By doing so, practical measures can be taken in real-time to meet unexpected high and low power demands thereby improving system reliability. / Sportevenemang, festivaler, byggarbetsplatser och film platser är exempel på fall där kraften krävs Tillfälligt eller och bort från elnätet. Tillfälliga Kraft Installationer avser system som inrättats för en begränsad tid med Vanligtvis ström genereras på plats. De flesta lastprognoser forskning har kretsat kring inställningar med permanent eller strömförsörjning (zoals i bostadshus). Tvärtom föreslår detta arbete maskininlärning metoder för att noggrant prognos belastning under Tillfälliga anläggningar. I praktiken är thesis Typiskt system drivs med dieselgeneratorer som är överdimensionerad och följaktligen arbetar ineffektivt vid låga belastningsnivåer. I denna avhandling är en ‘Pre-Event Casting’ Föreslagen metod för att ta itu med denna ineffektivitet genom att klassificera ett nytt tillfälligt ström Installation till ett kluster av installationer med liknande lastmönster. Genom att göra så, kan dimensioneringen av generatorer och kraftproduktion planering optimeras därigenom förbättra systemets effektivitet. Load prognoser för Tillfälliga Kraft installationer är ook användbar Medan en tillfällig ström Installationen är i drift. En ‘Prognoser Real-Time’ Föreslagen metod är att använda övervakade lastdata strömmas till en server att förutse belastningen två timmar eller mer i förväg. Genom att göra så, kan praktiska åtgärder vidtas i realtid för att möta oväntade höga och låga effektbehov och därigenom förbättra systemets tillförlitlighet.

Cette thèse s’intéresse à la problématique de la prédiction dans le cadre du design de systèmes énergétiques et des problèmes d’opération, et en particulier, à l’évaluation de l’adéquation de systèmes de production d’énergie renouvelables. L’objectif général est de développer une approche empirique pour générer des prédictions avec les incertitudes associées. En ce qui concerne cette direction de la recherche, une approche non paramétrique et empirique pour estimer les intervalles de prédiction (PIs) basés sur les réseaux de neurones (NNs) a été développée, quantifiant l’incertitude dans les prédictions due à la variabilité des données d’entrée et du comportement du système (i.e. due au comportement stochastique des sources renouvelables et de la demande d'énergie électrique), et des erreurs liées aux approximations faites pour établir le modèle de prédiction. Une nouvelle méthode basée sur l'optimisation multi-objectif pour estimer les PIs basée sur les réseaux de neurones et optimale à la fois en termes de précision (probabilité de couverture) et d’information (largeur d’intervalle) est proposée. L’ensemble de NN individuels par deux nouvelles approches est enfin présenté comme un moyen d’augmenter la performance des modèles. Des applications sur des études de cas réels démontrent la puissance de la méthode développée. / This Ph.D. work addresses the problem of prediction within energy systems design and operation problems, and particularly the adequacy assessment of renewable power generation systems. The general aim is to develop an empirical modeling framework for providing predictions with the associated uncertainties. Along this research direction, a non-parametric, empirical approach to estimate neural network (NN)-based prediction intervals (PIs) has been developed, accounting for the uncertainty in the predictions due to the variability in the input data and the system behavior (e.g. due to the stochastic behavior of the renewable sources and of the energy demand by the loads), and to model approximation errors. A novel multi-objective framework for estimating NN-based PIs, optimal in terms of both accuracy (coverage probability) and informativeness (interval width) is proposed. Ensembling of individual NNs via two novel approaches is proposed as a way to increase the performance of the models. Applications on real case studies demonstrate the power of the proposed framework.