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Previsão da carga de curto prazo de áreas elétricas através de técnicas de inteligência artificial.; Short term load forecasting in eletrical areas using artificial inteligence.

Guirelli, Cleber Roberto
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 30/11/2006 PT
Relevância na Pesquisa
66.83%
Hoje em dia, com a privatização e aumento da competitividade no mercado elétrico, as empresas precisam encontrar formas de melhorar a qualidade do serviço e garantir lucratividade. A previsão de carga de curto prazo é uma atividade indispensável à operação que pode melhorar a segurança e diminuir custos de geração. A fim de realizar a previsão da carga, é necessária a identificação de padrões de comportamento de consumo e da sua relação com variáveis exógenas ao sistema tais como condições climáticas. Originalmente o problema foi resolvido de forma matemática e estatística através de técnicas tais como as séries numéricas, que fornecem bons resultados, mas utilizam processos complexos e de difícil modelamento. O surgimento das técnicas de inteligência artificial forneceu uma nova ferramenta capaz de lidar com a grande massa de dados das cargas e inferir por si mesmo a relação entre as variáveis do sistema. Notadamente, as redes neurais e a lógica fuzzy se destacaram como as técnicas mais adequadas, sendo que já vem sendo estudadas e utilizadas para a previsão de carga a mais de 20 anos. Este trabalho apresenta uma metodologia para a previsão da curva de carga diária de áreas elétricas através do uso de técnicas de inteligência artificial...

A novel neural model to electrical load forecasting in transformers

De Souza, A. N.; Da Silva, I. N.; Ulson, Jose Alfredo Covolan; Bordon, M. E.; Callaos, N.; DaSilva, I. N.; Molero, J.
Fonte: Int Inst Informatics & Systemics Publicador: Int Inst Informatics & Systemics
Tipo: Conferência ou Objeto de Conferência Formato: 19-23
ENG
Relevância na Pesquisa
66.5%
The paper describes a novel neural model to electrical load forecasting in transformers. The network acts as identifier of structural features to forecast process. So that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through load data extracted from a Brazilian Electric Utility taking into account time, current, tension, active power in the three phases of the system. The results obtained in the simulations show that the developed technique can be used as an alternative tool to become more appropriate for planning of electric power systems.

Electrical load forecasting formulation by a fast neural network

Lopes, MLM; Minussi, C. R.; Lotufo, ADP
Fonte: C R L Publishing Ltd Publicador: C R L Publishing Ltd
Tipo: Artigo de Revista Científica Formato: 51-57
ENG
Relevância na Pesquisa
66.45%
The objective of this work is to develop a methodology for electric load forecasting based on a neural network. Here, backpropagation algorithm is used with an adaptive process that based on fuzzy logic and using a decaying exponential function to avoid instability in the convergence process. This methodology results in fast training, when compared to the conventional formulation of backpropagation algorithm. The results are presented using data from a Brazilian Electric Company, and shows a very good performance for the proposal objective.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

Lopes, MLM; Minussi, C. R.; Lotufo, ADP
Fonte: Elsevier B.V. Publicador: Elsevier B.V.
Tipo: Artigo de Revista Científica Formato: 235-244
ENG
Relevância na Pesquisa
66.56%
This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Multi-Agent Simulation of Urban Social Dynamics for Spatial Load Forecasting

Melo, Joel D.; Carreno, Edgar Manuel; Padilha-Feltrin, Antonio
Fonte: Institute of Electrical and Electronics Engineers (IEEE) Publicador: Institute of Electrical and Electronics Engineers (IEEE)
Tipo: Artigo de Revista Científica Formato: 1870-1878
ENG
Relevância na Pesquisa
66.68%
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES); Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); A multi-agent system for spatial electric load forecasting, especially suited to simulating the different social dynamics involved in distribution systems, is presented. This approach improves the spatial forecasting techniques that usually consider the service zone as a static entity to model or simulate the spatial electric load forecasting in a city. This paper aims to determine how the electric load will be distributed among the sub-zones in the city. For this, the service zone is divided into several subzones, each subzone considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented through development probabilities. These probabilities are considered as input data for the simulation. Given this setting, different kinds of agents can be developed to simulate the growth pattern of the loads in distribution systems in parallel. The approach is tested with data from a real distribution system in a mid-size city; the results show a low spatial error when compared to real data. Less than 6% of the load growth was identified 0.71 km outside of its correct location on the test system.

A Cellular Automaton Approach to Spatial Electric Load Forecasting

Carreno, Edgar Manuel; Rocha, Rodrigo Mazo; Padilha-Feltrin, Antonio
Fonte: Institute of Electrical and Electronics Engineers (IEEE) Publicador: Institute of Electrical and Electronics Engineers (IEEE)
Tipo: Artigo de Revista Científica Formato: 532-540
ENG
Relevância na Pesquisa
66.5%
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); A method for spatial electric load forecasting using a reduced set of data is presented. The method uses a cellular automata model for the spatiotemporal allocation of new loads in the service zone. The density of electrical load for each of the major consumer classes in each cell is used as the current state, and a series of update rules are established to simulate S-growth behavior and the complementarity among classes. The most important features of this method are good performance, few data and the simplicity of the algorithm, allowing for future scalability. The approach is tested in a real system from a mid-size city showing good performance. Results are presented in future preference maps.

Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network

Nose-Filho, Kenji; Plasencia Lotufo, Anna Diva; Minussi, Carlos Roberto
Fonte: Institute of Electrical and Electronics Engineers (IEEE) Publicador: Institute of Electrical and Electronics Engineers (IEEE)
Tipo: Artigo de Revista Científica Formato: 2862-2869
ENG
Relevância na Pesquisa
66.76%
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, a technique that is precise, reliable, and has short-time processing is necessary. This paper uses two methodologies for short-term multinodal load forecasting. The first individually forecasts the local loads and the second forecasts the global load and individually forecasts the load participation factors to estimate the local loads. For the forecasts, a modified general regression neural network and a procedure to automatically reduce the number of inputs of the artificial neural networks are proposed. To design the forecasters, the previous study of the local loads was not necessary, thus reducing the complexity of the multinodal load forecasting. Tests were carried out by using a New Zealand distribution subsystem and the results obtained were found to be compatible with those available in the specialized literature.

Multi-Agent Framework for Spatial Load Forecasting

Melo, J. D.; Carreno, E. M.; Padilha-Feltrin, A.
Fonte: IEEE Publicador: IEEE
Tipo: Conferência ou Objeto de Conferência Formato: 8
ENG
Relevância na Pesquisa
66.5%
A multi-agent framework for spatial electric load forecasting, especially suited to simulate the different dynamics involved on distribution systems, is presented. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented as development probabilities. With this setting, different kind of agents can be developed to simulate the growth pattern of the loads in distribution systems. This paper presents two different kinds of agents to simulate different situations, presenting some promissory results.

A fast electric load forecasting using neural networks

Lopes, Mara Lúcia M.; Minussi, Carlos R.; Lotufo, Anna Diva P.
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência Formato: 646-649
ENG
Relevância na Pesquisa
66.45%
The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.

A fast electric load forecasting using adaptive neural networks

Lopes, M. L M; Lotufo, A. D P; Minussi, C. R.
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência Formato: 362-367
ENG
Relevância na Pesquisa
66.6%
This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

Spatial load forecasting using a demand propagation approach

Melo, J. D.; Carreno, E. M.; Padilha-Feltrin, A.
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência Formato: 196-203
ENG
Relevância na Pesquisa
66.77%
A method for spatial electric load forecasting using multi-agent systems, especially suited to simulate the local effect of special loads in distribution systems is presented. The method based on multi-agent systems uses two kinds of agents: reactive and proactive. The reactive agents represent each sub-zone in the service zone, characterizing each one with their corresponding load level, represented in a real number, and their relationships with other sub-zones represented in development probabilities. The proactive agent carry the new load expected to be allocated because of the new special load, this agent distribute the new load in a propagation pattern. The results are presented with maps of future expected load levels in the service zone. The method is tested with data from a mid-size city real distribution system, simulating the effect of a load with attraction and repulsion attributes. The method presents good results and performance. © 2011 IEEE.

Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

Nose-Filho, K.; Lotufo, A. D P; Minussi, C. R.
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
66.73%
This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.

Considering Urban Dynamics in spatial electric load forecasting

Melo, J. D.; Carreno, E. M.; Padilha-Feltrin, A.
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
66.69%
When dealing with spatio-temporal simulations of load growth inside a service zone, one of the most important problems faced by a Distribution Utility is how to represent the different relationships among different areas. A new load in a certain part of the city could modify the load growth in other parts of the city, even outside of its radius of influence. These interactions are called Urban Dynamics. This work aims to discuss how to implement Urban Dynamics considerations into the spatial electric load forecasting simulations using multi-agent simulations. To explain the approach, three examples are introduced, including the effect of an attraction load, the effect of a repulsive load, and the effect of several attraction/repulsive loads at the same time when considering the natural load growth. © 2012 IEEE.

Determining spatial resolution in spatial load forecasting using a grid-based model

Melo, Joel D.; Carreno, Edgar M.; Calvino, Aida; Padilha-Feltrin, Antonio
Fonte: Elsevier B.V. Publicador: Elsevier B.V.
Tipo: Artigo de Revista Científica Formato: 177-184
ENG
Relevância na Pesquisa
66.71%
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES); Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); This paper presents a grid-based model that aims to find a suitable spatial resolution to improve visualization and inference of the results of spatial load forecasting for feeders and/or distribution transformers. This approach can be considered as an unsupervised learning approach to cluster the input data (i.e., the power rating of the distribution transformers) in cells (clusters) to find a cell size that gives high internal homogeneity in the cells and high external heterogeneity of each cell with respect to its neighbors in order to reduce the inference errors that can affect the results of spatial load forecasting methods. The proposal was tested considering the spatial distribution of transformers installed in a real distribution system for a medium-sized city. Using the resolution determined by the grid-based model, it is possible to build a map of the spatial distribution of load density in a service area with a low relative local dispersion and a high relative global dispersion. To demonstrate the efficacy of the approach, spatial electric load forecasting of the study zone is performed using different spatial resolutions; the grid size determined via the proposed model represents the equilibrium between spatial error and computational effort...

Previsão de carga de curto prazo usando ensembles de previsores selecionados e evoluidos por algoritmos geneticos; Short-term load forecasting using esembles of selected and evolved predictors by genetic algorithms

Marcos de Almeida Leone Filho
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 31/01/2006 PT
Relevância na Pesquisa
66.53%
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...

Chaos theory applied to input space representation of autonomous neural network-based short-term load forecasting models

Ferreira,Vitor Hugo; Silva,Alexandre Pinto Alves da
Fonte: Sociedade Brasileira de Automática Publicador: Sociedade Brasileira de Automática
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/12/2011 EN
Relevância na Pesquisa
66.62%
After 1991, the literature on load forecasting has been dominated by neural network based proposals. However, one major risk in using neural models is the possibility of excessive training, i.e., data overfitting. The extent of nonlinearity provided by neural network based load forecasters, which depends on the input space representation, has been adjusted using heuristic procedures. The empirical nature of these procedures makes their application cumbersome and time consuming. Autonomous modeling including automatic input selection and model complexity control has been proposed recently for short-term load forecasting. However, these techniques require the specification of an initial input set that will be processed by the model in order to select the most relevant variables. This paper explores chaos theory as a tool from non-linear time series analysis to automatic select the lags of the load series data that will be used by the neural models. In this paper, Bayesian inference applied to multi-layered perceptrons and relevance vector machines are used in the development of autonomous neural models.

Local regression-based-short term load forecasting

Zivanovic, R.
Fonte: Kluwer Academic Publ Publicador: Kluwer Academic Publ
Tipo: Artigo de Revista Científica
Publicado em //2001 EN
Relevância na Pesquisa
66.66%
This paper presents a novel method for short-term load forecasting based on local polynomial regression. Before applying the local regression, data mining algorithm selects historic load sequences satisfying known factors that are characterising required load model. Further on, the selected sequences are pre-processed with robust location estimator (M-estimator) in order to reduce serial correlation and to eliminate outliers in historic data. On pre-processed load data we applied locally a truncated Taylor expansion to approximate functional relationship between load and load-affecting factors. Two methods for selecting optimal smoothing parameters (window size and polynomial degree) for local approximations are presented in the paper. These algorithms offer to us close insight into trade-off between bias and variance of the local approximations. In that way, they are able to help in selecting smoothing parameters locally (for each local fit) to fulfil the load modelling requirements. An example is presented at the end of this paper that clearly demonstrates the main features of this method.; The original publication can be found at www.springerlink.com

Estimation of a preference map of new consumers for spatial load forecasting simulation methods using a spatial analysis of points

Melo, Joel D.; Carreno, Edgar M.; Padilha-Feltrin, Antonio
Fonte: Elsevier B.V. Publicador: Elsevier B.V.
Tipo: Artigo de Revista Científica Formato: 299-305
ENG
Relevância na Pesquisa
66.66%
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); Processo FAPESP: 303817/2012-7; Processo FAPESP: 473679/2013-2; Processo FAPESP: 2014/06629-0; The paper presents a spatial analysis of points especially suited to estimate a preference map for new consumers, which is then used as an analytical tool in spatial electric load forecasting. This approach is an exploratory spatial data analysis used to discover useful point patterns in the spatial location of distribution transformers to calculate a preference value for each area, rating it with respect to a hypothetical load change that may occur. We consider the locations of distribution transformers occupied land. Random points are generated in the study area where the new loads are expected; these points are referred to as unoccupied land. The method uses a generalized additive model (GAM) to estimate the probability of unoccupied land becoming occupied land. We test the approach with data from a real distribution system in a mid-size city in Brazil; the result is a preference map that shows the areas where new consumers are most likely to be allocated. The main advantage of this method is the ability work with a small-scale resolution...

Uma abordagem para a previsão de carga crítica do sistema elétrico brasileiro = An approach for critical load forecasting of Brazilian power system; An approach for critical load forecasting of Brazilian power system

Mateus Neves Barreto
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 07/03/2014 PT
Relevância na Pesquisa
56.67%
O Sistema Elétrico Brasileiro abastece cerca de 97% da demanda de energia nacional. Frente ao extenso território brasileiro, necessita-se de um sistema de transmissão de larga escala, devido as grandes distâncias entre as gerações, das hidroelétricas, e a principal concentração da demanda, no Sudeste brasileiro. Para garantir segurança e economia da operação do Sistema Elétrico Brasileiro são realizadas análises da operação do sistema de geração e transmissão frente às condições de cargas críticas. A ideia é preparar o sistema para suportar as condições mais severas de carga. A curva de carga crítica é calculada para cada mês com discretização horária (ou menor). A mesma é composta pela carga mínima observada num dado mês no período da primeira a oitava hora, e pela carga máxima observada no mês para as horas restantes. Utilizando históricos de demanda pertencentes aos agentes do Setor Elétrico Brasil, foi possível criar um histórico de cinco anos, 60 meses, de curvas de carga crítica. Esses dados foram disponibilizados pelo Operador Nacional do Sistema Elétrico Brasileiro ¿ ONS, em conjunto com o desenvolvimento de um projeto de pesquisa, através de um sistema de suporte a decisão nomeado SysPrev. Nesta dissertação são propostos três modelos para realizar a previsão da curva de carga crítica. Dois modelos utilizam Redes Neurais Artificiais e um modelo utiliza Suavização Exponencial de Holt-Winters (HW). Os resultados obtidos por todos os modelos foram satisfatórios. O modelo de Suavização Exponencial se destacou perante os outros dois modelos atingindo erros médios absolutos próximos a 3%. Esses resultados justificam-se devido às séries históricas de curvas de carga crítica possuírem características de tendência e sazonalidade e o modelo de HW ser projetado especificamente para séries temporais com estas características.; The Brazilian Power System supplies around 97 % of national energy demand. By reason of the broad Brazilian territory...

Spatial electric load forecasting using an evolutionary heuristic

Carreno,E. M.; Padilha-Feltrin,A.; Leal,A. G.
Fonte: Sociedade Brasileira de Automática Publicador: Sociedade Brasileira de Automática
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/08/2010 EN
Relevância na Pesquisa
66.45%
A method for spatial electric load forecasting using elements from evolutionary algorithms is presented. The method uses concepts from knowledge extraction algorithms and linguistic rules' representation to characterize the preferences for land use into a spatial database. The future land use preferences in undeveloped zones in the electrical utility service area are determined using an evolutionary heuristic, which considers a stochastic behavior by crossing over similar rules. The method considers development of new zones and also redevelopment of existing ones. The results are presented in future preference maps. The tests in a real system from a midsized city show a high rate of success when results are compared with information gathered from the utility planning department. The most important features of this method are the need for few data and the simplicity of the algorithm, allowing for future scalability.