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Book Electrical Load Forecasting

Download or read book Electrical Load Forecasting written by S.A. Soliman and published by Elsevier. This book was released on 2010-05-26 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: Succinct and understandable, this book is a step-by-step guide to the mathematics and construction of electrical load forecasting models. Written by one of the world’s foremost experts on the subject, Electrical Load Forecasting provides a brief discussion of algorithms, their advantages and disadvantages and when they are best utilized. The book begins with a good description of the basic theory and models needed to truly understand how the models are prepared so that they are not just blindly plugging and chugging numbers. This is followed by a clear and rigorous exposition of the statistical techniques and algorithms such as regression, neural networks, fuzzy logic, and expert systems. The book is also supported by an online computer program that allows readers to construct, validate, and run short and long term models. Step-by-step guide to model construction Construct, verify, and run short and long term models Accurately evaluate load shape and pricing Creat regional specific electrical load models

Book Artificial neural networks fuzzy logic and neuro fuzzy system in the role of short term load forecast

Download or read book Artificial neural networks fuzzy logic and neuro fuzzy system in the role of short term load forecast written by and published by . This book was released on 1908 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Esta dissertação investiga o desempenho de técnicas de inteligência computacional na previsão de carga em curto prazo. O objetivo deste trabalho foi propor e avaliar sistemas de redes neurais, lógica nebulosa, neuro-fuzzy e híbridos para previsão de carga em curto prazo, utilizando como entradas variáveis que influenciam o comportamento da carga, tais como: temperatura, índice de conforto e perfil de consumo. Este trabalho envolve 4 etapas principais: um estudo sobre previsão de carga e sobre as variáveis que influenciam o comportamento da carga; um estudo da aplicação de técnicas de inteligência computacional em previsão de carga; a definição de sistemas de redes neurais, lógica fuzzy e neuro-fuzzy em previsão de carga; e estudo de casos. No estudo sobre previsão de carga, foi observada a influência de algumas variáveis no comportamento da curva de carga de uma empresa de energia elétrica. Entre estas variáveis se encontram alguns dados meteorológicos (Temperatura, Umidade, Luminosidade, Índice de conforto, etc.), além de informações sobre o perfil de consumo de carga das empresas. Também foi observado o comportamento da série de carga com relação ao dia da semana, sua sazonalidade e a correlação entre o valor atual e valores passados. Foi realizado um levantamento bibliográfico sobre a aplicação de técnicas de inteligência computacional na previsão de carga. Os modelos de redes neurais, são os mais explorados até o momento. Os modelos de lógica fuzzy começaram a ser utilizados mais recentemente. Modelos neuro-fuzzy são mais recentes que os demais, não existindo portanto, muita bibliografia a respeito. Os projetos de aplicação dos três modelos foram classificados quanto à sua arquitetura, desempenho, erros medidos, entradas utilizadas e horizonte da previsão. Foram propostos e implementados 4 sistemas de previsão de carga: lógica fuzzy, redes neurais, sistema neuro-fuzzy hierárquico e um sistema híbrido neural/neuro-fuzzy. Os sistemas foram especializados para cada dia da semana, pelo fato do comportamento da carga ser distinto entre estes dias. Para os sistemas neural, neuro-fuzzy e híbrido os dados também foram separados em inverno e verão, pois o perfil de consumo de carga é diferente nestas estações. O sistema com lógica fuzzy foi modelado para realizar previsões de curtíssimo prazo (10 em 10 minutos), utilizando para isto o histórico de carga, hora do dia e intervalo de dez minutos dentro da hora do dia. As regras do sistema foram geradas automaticamente a partir do histórico de carga e os conjuntos nebulosos foram pré-definidos. O sistema com redes neurais teve sua arquitetura definida através de experimentos, utilizando-se apenas dados de carga, hora do dia e mês como entradas. O modelo de rede escolhido foi com retropropagação do erro (backpropagation). Foram realizados testes incluindo outras entradas como temperatura e perfil de consumo. Para o sistema neuro-fuzzy foi escolhido um sistema neuro-fuzzy hierárquico, que define automaticamente sua estrutura e as regras a partir do histórico dos dados. Em uma última etapa, foi estudado um sistema híbrido neural/ neuro-fuzzy, no qual a previsão da rede neural é uma entrada do sistema neuro-fuzzy. Para os três últimos modelos as previsões realizadas foram em curto prazo, com um horizonte de uma hora Os sistemas propostos foram testados em estudos de casos e os resultados comparados entre si e com os resultados obtidos em outros projetos na área. Os dados de carga utilizados no sistema com lógica fuzzy foram da CEMIG, no período de 1994 a 1996, em intervalos de 10 minutos, para previsões em curtíssimo prazo. Os resultados obtidos podem ser considerados bons em comparação com um sistema de redes neurais utilizando os mesmos dados. Para os demais modelos foram utilizados os seguintes dados: dados horários de carga da Light e da CPFL, no período de 1996 a 1998; dados de temperatura (horária para região de atuação da Light e diária para a região da CPFL) no período de 1996 a 1998; a codificação do mês e hora do dia; e um perfil de carga por classe de consumo, para realizar previsões de curto prazo (1 hora, 24 passos a frente). Os dados foram separados em inverno e verão, além de dia da semana, o que torna os modelos bastante especializados. Os resultados obtidos pelos modelos foram da ordem de 0,0 % para o sistema com lógica fuzzy, 0,0 % para redes neurais, 0,0 % para o sistema neuro-fuzzy e 0,0 % para o sistema híbrido. Este trabalho verificou a aplicabilidade das técnicas de inteligência computacional na previsão de carga, demostrando que um estudo preliminar das séries a serem previstas e a sua relação com outras variáveis tem forte influência sobre as previsões.

Book Electric Load Forecasting Using an Artificial Neural Networks

Download or read book Electric Load Forecasting Using an Artificial Neural Networks written by Natalia Gotman and published by LAP Lambert Academic Publishing. This book was released on 2014-03 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Electric load forecasting is an important research field in electric power industry. It plays a crucial role in solving a wide range of tasks of short-term planning and operating control of electric power system operating modes. Load forecasting is carried out in different time spans. Load forecasting within a current day - operating forecasting; one-day-week-month-ahead load forecasting - short-term load forecasting; one-month-quarter-year-ahead load forecasting - long-term load forecasting. So far a great number of both conventional and non-conventional electric load forecasting methods and models have been developed. The work presents research results of electric load forecasting for electrical power systems using artificial neural networks and fuzzy logic as one of the most advanced and perspective directions of solving this task. A theoretical approach to the issues discussed is combined with the data of experimental studies implemented with application of load curves of regional electrical power systems. The book is addressed to specialists and researchers concerned with operational control modes of electric power systems.

Book Core Concepts and Methods in Load Forecasting

Download or read book Core Concepts and Methods in Load Forecasting written by Stephen Haben and published by Springer Nature. This book was released on 2023-06-01 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive open access book enables readers to discover the essential techniques for load forecasting in electricity networks, particularly for active distribution networks. From statistical methods to deep learning and probabilistic approaches, the book covers a wide range of techniques and includes real-world applications and a worked examples using actual electricity data (including an example implemented through shared code). Advanced topics for further research are also included, as well as a detailed appendix on where to find data and additional reading. As the smart grid and low carbon economy continue to evolve, the proper development of forecasting methods is vital. This book is a must-read for students, industry professionals, and anyone interested in forecasting for smart control applications, demand-side response, energy markets, and renewable utilization.

Book Short term Electric Load Forecasting by Using Multi layer Feed forward Neural Network

Download or read book Short term Electric Load Forecasting by Using Multi layer Feed forward Neural Network written by Marvin Herbert Wibisono and published by . This book was released on 2004 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Short term Electric Load Forecasting Using Neural Network with Fuzzy Set Based Classification

Download or read book Short term Electric Load Forecasting Using Neural Network with Fuzzy Set Based Classification written by Gumpanart Bumroonggit and published by . This book was released on 1995 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book A short term load forecasting model using neural network and fuzzy logic

Download or read book A short term load forecasting model using neural network and fuzzy logic written by and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: O objetivo principal desta dissertação é desenvolver um método de previsão de carga elétrica de curto prazo (previsão horária), através de um sistema híbrido(Redes Neurais e Lógica Fuzzy) utilizando temperaturas máximas e mínimas como variáveis explicativas. Como primeiro passo, foram definidos os perfis homogêneos das curvas de carga diárias através de um classificador utilizando os Mapas Auto Organizáveis (Self-Organizing Maps-SOM). Um previsor será adicionado ao esquema de previsão através da Lógica Fuzzy que associará as variáveis climáticas aos perfis criados pela SOM produzindo as previsões. O modelo foi aplicado em dados de duas concessionárias de energia elétrica do Brasil usando dados horários coletados durante dois anos.

Book Short term Load Forecasting Using Fuzzy Neural Networks

Download or read book Short term Load Forecasting Using Fuzzy Neural Networks written by Haiwu Ma and published by . This book was released on 1994 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Short term Electric Load Forecasting Using Neural Networks

Download or read book Short term Electric Load Forecasting Using Neural Networks written by and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book On Short Term Load Forecasting Using Machine Learning Techniques

Download or read book On Short Term Load Forecasting Using Machine Learning Techniques written by Behnam Farsi and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since electricity plays a crucial role in industrial infrastructures of countries, power companies are trying to monitor and control infrastructures to improve energy management, scheduling and develop efficiency plans. Smart Grids are an example of critical infrastructure which can lead to huge advantages such as providing higher resilience and reducing maintenance cost. Due to the nonlinear nature of electric load data there are high levels of uncertainties in predicting future load. Accurate forecasting is a critical task for stable and efficient energy supply, where load and supply are matched. However, this non-linear nature of loads presents significant challenges for forecasting. Many studies have been carried out on different algorithms for electricity load forecasting including; Deep Neural Networks, Regression-based methods, ARIMA and seasonal ARIMA (SARIMA) which among the most popular ones. This thesis discusses various algorithms analyze their performance for short-term load forecasting. In addition, a new hybrid deep learning model which combines long short-term memory (LSTM) and a convolutional neural network (CNN) has been proposed to carry out load forecasting without using any exogenous variables. The difference between our proposed model and previously hybrid CNN-LSTM models is that in those models, CNN is usually used to extract features while our proposed model focuses on the existing connection between LSTM and CNN. This methodology helps to increase the model's accuracy since the trend analysis and feature extraction process are accomplished, respectively, and they have no effect on each other during these processes. Two real-world data sets, namely "hourly load consumption of Malaysia" as well as "daily power electric consumption of Germany", are used to test and compare the presented models. To evaluate the performance of the tested models, root mean squared error (RMSE), mean absolute percentage error (MAPE) and R-squared were used. The results show that deep neural networks models are good candidates for being used as short-term prediction tools. Moreover, the proposed model improved the accuracy from 83.17\% for LSTM to 91.18\% for the German data. Likewise, the proposed model's accuracy in Malaysian case is 98.23\% which is an excellent result in load forecasting. In total, this thesis is divided into two parts, first part tries to find the best technique for short-term load forecasting, and then in second part the performance of the best technique is discussed. Since the proposed model has the best performance in the first part, this model is challenged to predict the load data of next day, next two days and next 10 days of Malaysian data set as well as next 7 days, next 10 days and next 30 days of German data set. The results show that the proposed model also has performed well where the accuracy of 10 days ahead of Malaysian data is 94.16\% and 30 days ahead of German data is 82.19\%. Since both German and Malaysian data sets are highly aggregated data, a data set from a research building in France is used to challenge the proposed model's performance. The average accuracy from the French experiment is almost 77\% which is reasonable for such a complex data without using any auxiliary variables. However, as Malaysian data and French data includes hourly weather data, the performance of the model after adding weather is evaluated to compare them before using weather data. Results show that weather data can have a positive influence on the model. These results show the strength of the proposed model and how much it is stable in front of some challenging tasks such as forecasting in different time horizons using two different data sets and working with complex data.

Book Hourly Load Forecasting by Neural Networks

Download or read book Hourly Load Forecasting by Neural Networks written by Rey-Chue Hwang and published by . This book was released on 1993 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Short term Electric Load Forecasting Using Artificial Neural Networks

Download or read book Short term Electric Load Forecasting Using Artificial Neural Networks written by Eric Lee Daugherty and published by . This book was released on 1994 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: