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Performance Evaluation Of New And Advanced Neural Networks For Short Term Load Forecasting
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Book Synopsis Performance Evaluation of New and Advanced Neural Networks for Short Term Load Forecasting by : Syed Talha Mehmood
Download or read book Performance Evaluation of New and Advanced Neural Networks for Short Term Load Forecasting written by Syed Talha Mehmood and published by . This book was released on 2014 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT: Electric power systems are huge real time energy distribution networks where accurate short term load forecasting (STLF) plays an essential role. This thesis is an effort to comprehensively investigate new and advanced neural network (NN) architectures to perform STLF. Two hybrid and two 3-layered NN architectures are introduced. Each network is individually tested to generate weekday and weekend forecasts using data from three jurisdictions of Canada. Overall findings suggest that 3-layered cascaded NN have outperformed almost all others for weekday forecasts. For weekend forecasts 3-layered feed forward NN produced most accurate results. Recurrent and hybrid networks performed well during peak hours but due to occurrence of constant high error spikes were not able to achieve high accuracy.
Book Synopsis Recurrent Neural Networks for Short-Term Load Forecasting by : Filippo Maria Bianchi
Download or read book Recurrent Neural Networks for Short-Term Load Forecasting written by Filippo Maria Bianchi and published by Springer. This book was released on 2017-11-09 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.
Book Synopsis Load Forecasting Using Artificial Neural Network by : Pradeepta Kumar Sarangi
Download or read book Load Forecasting Using Artificial Neural Network written by Pradeepta Kumar Sarangi and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Short Term Load Forecasting (STLF) is an essential and integral component for energy management and distribution corporations. Accurate load forecasting not only increases the efficiency of a distribution system, but also saves money. STLF is essential in taking quick decisions for a short period such as, for the next few hours or next day or a week. It helps to prevent load shedding and evaluations of various sophisticated financial products on energy pricing. It is important to consider that the forecasting is neither too optimistic nor too conservative because optimistic forecasting creates generation of surplus energy, causing loss of money and resources. Similarly, conservative forecasting will lead to shortage of energy requirement and hence will be a cause for power blackout. This paper examines and analyzes the use of Artificial Neural Network (ANN) as forecasting tools for predicting the future electric load demand and the impact of different numbers of neurons in the hidden layer in a three layered ANN architecture.
Book Synopsis Short-Term Load Forecasting by Artificial Intelligent Technologies by : Wei-Chiang Hong
Download or read book Short-Term Load Forecasting by Artificial Intelligent Technologies written by Wei-Chiang Hong and published by MDPI. This book was released on 2019-01-29 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies
Book Synopsis On Short-Term Load Forecasting Using Machine Learning Techniques by : Behnam Farsi
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 Synopsis Short-Term Load Forecasting 2019 by : Antonio Gabaldón
Download or read book Short-Term Load Forecasting 2019 written by Antonio Gabaldón and published by MDPI. This book was released on 2021-02-26 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.
Book Synopsis Short term load forecasting - an attempt to use artificial neural networks by :
Download or read book Short term load forecasting - an attempt to use artificial neural networks written by and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A previsão de perfis de carga elétrica (i.e., das séries de cargas a cada hora de um dia) tem sido freqüentemente tentada por meio de modelos baseados em redes neurais. Os resultados conseguidos por estes modelos, contudo, ainda não são considerados inteiramente convincentes. Há duas razões para ceticismo: em primeiro lugar, os modelos sugeridos geralmente se baseiam em redes que parecem ser complexas demais em relação aos dados que pretendem modelar (isto é, estes modelos parecem estar superparametrizados); em segundo lugar, estes modelos geralmente não são bem validados, pois os artigos que os propõem não comparam o desempenho das redes ao de modelos de referência. Nesta tese, examinamos estes dois pontos por meio de revisões críticas da literatura e de simulações, a fim de verificar se é realmente viável a aplicação de redes neurais à previsão de perfis de carga. Nas simulações, construímos modelos bastante complexos de redes e verificamos empiricamente sua validade, pela comparação de seu desempenho preditivo fora da amostra de treino ao desempenho de vários outros modelos de previsão. Os resultados mostram que as redes, mesmo quando muito complexas, conseguem previsões de perfis mais acuradas do que os modelos tradicionais, o que sugere que elas poderão trazer uma grande contribuição para a solução do problema de previsão de cargas.
Book Synopsis Neural networks in load forecasting in electric energy systems by :
Download or read book Neural networks in load forecasting in electric energy systems 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 a utilização de Redes Neurais Artificiais (RNAs) na área de previsão de carga elétrica. Nesta investigação foram utilizados dados reais de energia relativos ao sistema elétrico brasileiro. O trabalho consiste de quatro partes principais: um estudo sobre o problema de previsão de carga no contexto de sistemas elétricos de potência; o estudo e a modelagem das RNAs para previsão de carga; o desenvolvimento do ambiente de simulação; e o estudo de casos. O estudo sobre o problema de previsão de carga envolveu uma investigação sobre a importância da previsão de demanda de energia na área de sistemas elétricos de potência. Enfatizou-se a classificação dos diversos tipos de previsão de acordo com o seu horizonte, curto e longo prazo, bem como a análise das variáveis mais relevantes para a modelagem da carga elétrica. O estudo também consistiu da análise de vários projetos na área de previsão de carga, apresentando as metodologias mais utilizadas. O estudo e a modelagem de RNAs na previsão de carga envolveu um extenso estudo bibliográfico de diversas metodologias. Foram estudadas as arquiteturas e os algoritmos de aprendizado mais empregados. Constatou-se uma predominância da utilização do algoritmo de retropropagação (Backpropagation) nas aplicações de previsão de carga elétrica horária para curto prazo. A partir desse estudo, e utilizando o algoritmo de retropropagação, foram propostas diversas arquiteturas de RNAs de acordo com o tipo de previsão desejada. O desenvolvimento do ambiente de simulação foi implementado em linguagem C em estações de trabalho SUN. O pacote computacional engloba basicamente 3 módulos: um módulo de pré-processamento da série de carga para preparar os dados de entrada; um módulo de treinamento da Rede Neural para o aprendizado do comportamento da série; e um módulo de execução da Rede Neural para a previsão dos valores futuros da série. A construção de uma interface amigável para a execução do sistema de previsão, bem como a obtenção de um sistema portátil foram as metas principais para o desenvolvimento do simulador. O estudo de casos consistiu de um conjunto de implementações com o objetivo de testar o desempenho de um sistema de previsão baseado em Redes Neurais para dois horizontes distintos: previsão horária e previsão mensal. No primeiro caso, foram utilizados dados de energia da CEMIG (Estado de Minas Gerais) e LIGHT (Estado do Rio de Janeiro). No segundo caso, foram utilizados dados de energia de 32 companhias do setor elétrico brasileiro. Destaca-se que a previsão mensal faz parte de um projeto de interesse da ELETROBRÁS, contratado pelo CEPEL. Para ambos os casos, investigou-se a influência do horizonte de previsão e da época do ano no desempenho do sistema de previsão. Além disso, foram estudadas as variações do desempenho das Redes Neurais de acordo com a empresa de energia elétrica utilizada. A avaliação do desempenho foi feita através da análise das seguintes estatísticas de erro: MAPE (Mean Absolute Percentage Error), RMSE (Root Mean Square Error) e U de Theil. O desempenho das RNAs foi comparado com o de outras técnicas de previsão, como os métodos de Holt-Winters e Box & Jenkins, obtendo-se resultados, em muitos casos, superiores.
Book Synopsis Hybrid Advanced Techniques for Forecasting in Energy Sector by : Wei-Chiang Hong
Download or read book Hybrid Advanced Techniques for Forecasting in Energy Sector written by Wei-Chiang Hong and published by MDPI. This book was released on 2018-10-19 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Hybrid Advanced Techniques for Forecasting in Energy Sector" that was published in Energies
Book Synopsis Neural Networks for Pattern Recognition by : Christopher M. Bishop
Download or read book Neural Networks for Pattern Recognition written by Christopher M. Bishop and published by Oxford University Press. This book was released on 1995-11-23 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical pattern recognition; Probability density estimation; Single-layer networks; The multi-layer perceptron; Radial basis functions; Error functions; Parameter optimization algorithms; Pre-processing and feature extraction; Learning and generalization; Bayesian techniques; Appendix; References; Index.
Author :Management Association, Information Resources Publisher :IGI Global ISBN 13 :1668424096 Total Pages :1575 pages Book Rating :4.6/5 (684 download)
Book Synopsis Research Anthology on Artificial Neural Network Applications by : Management Association, Information Resources
Download or read book Research Anthology on Artificial Neural Network Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2021-07-16 with total page 1575 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing phenomenon as more realize the plethora of uses and benefits they provide. Due to their complexity, it is vital for researchers to understand ANN capabilities in various fields. The Research Anthology on Artificial Neural Network Applications covers critical topics related to artificial neural networks and their multitude of applications in a number of diverse areas including medicine, finance, operations research, business, social media, security, and more. Covering everything from the applications and uses of artificial neural networks to deep learning and non-linear problems, this book is ideal for computer scientists, IT specialists, data scientists, technologists, business owners, engineers, government agencies, researchers, academicians, and students, as well as anyone who is interested in learning more about how artificial neural networks can be used across a wide range of fields.
Book Synopsis Artificial neural networks, fuzzy logic and neuro-fuzzy system in the role of short term load forecast by :
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 Synopsis Neural Network Based Load and Price Forecasting and Confidence Interval Estimation in Deregulated Power Markets by : Li Zhang
Download or read book Neural Network Based Load and Price Forecasting and Confidence Interval Estimation in Deregulated Power Markets written by Li Zhang and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman filter (EKF) can be used as an integrated adaptive learning and confidence interval estimation algorithm for neural networks, with fast convergence and small confidence intervals. However, EKF learning is computationally expensive because it involves high dimensional matrix manipulations. A modified U-D factorization within the decoupled EKF (DEKF-UD) framework is developed in this research. The computational efficiency and numerical stability are significantly improved.
Book Synopsis Short-Term Load Forecasting by Artificial Intelligent Technologies by : Guo-Feng Fan
Download or read book Short-Term Load Forecasting by Artificial Intelligent Technologies written by Guo-Feng Fan and published by . This book was released on 2019 with total page 1 pages. Available in PDF, EPUB and Kindle. Book excerpt: In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on). Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems.
Book Synopsis Neural Networks as Cybernetic Systems by : Holk Cruse
Download or read book Neural Networks as Cybernetic Systems written by Holk Cruse and published by Thieme Medical Publishers. This book was released on 1996 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Wavelet Neural Network Based Very Short-term Load Forecasting and Prediction Interval Estimation by : Che Guan
Download or read book Wavelet Neural Network Based Very Short-term Load Forecasting and Prediction Interval Estimation written by Che Guan and published by . This book was released on 2013 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Short Term Load Forecasting Using Artificial Neural Networks by : Syed Mahmood Ahmed
Download or read book Short Term Load Forecasting Using Artificial Neural Networks written by Syed Mahmood Ahmed and published by . This book was released on 1992 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:
