EM - Escola de Minas

Resultados da Pesquisa

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NeuroDem - a neural network based short term demand forecaster.
(2001) Silva, Alexandre Pinto Alves da; Rodrigues, Ubiratan de Paula; Reis, Agnaldo José da Rocha; Moulin, Luciano Souza
The application of Neural Network (NN) based Short-Term Load Forecasting (STLF) has developed to sophisticated practical systems over the years. However, the question of how to maximize the generalization ability of such machines, together with the choice of architecture, activation functions, training set data and size, etc. makes up a huge number of possible combinations for the final NN design, whose optimal solution has not been figured yet. This paper describes a STLF system (NeuroDem) which has been used by Brazilian electric utilities for 3 years. It uses a non-parametric model based on a linear model coupled with a polynomial network, identified by pruninglgrowing mechanisms. NeuroDem has special features of data pre-processing and confidence intervals calculations, which are also described. Results of load forecasts are presented for one year with forecasting horizons from 15 min. to 168 hours ahead.
Enhancing neural network based load forecasting via preprocessing.
(2001) Silva, Alexandre Pinto Alves da; Reis, Agnaldo José da Rocha; El-Sharkawi, Mohamed A.; Marks II, Robert J.
The importance of Short-Term Load Forecasting (STLF) has increased, lately. With deregulation and competition, energy price forecasting has become a big business. Load bus forecasting is essential for feeding the analytical methods used for determining energy prices. The variability and nonstationarity of loads are getting worse due to the dynamics of energy tariffs. Besides, the number of nodal loads to be predicted does not allow frequent interventions from load forecasting specialists. More autonomous load predictors are needed in the new competitive scenario. Despite the success of neural network based STLF, techniques for preprocessing the load data have been overlooked. In this paper, different techniques for preprocessing a load series have been investigated. The main goal is to induce stationarity and to emphasize the relevant features of the series in order to produce more robust load forecasters. One year of load data from a Brazilian electric utility has been used to validate the proposed
Artificial neural network-based short-term demand forecaster.
(2003) Silva, Alexandre Pinto Alves da; Rodrigues, Ubiratan de Paula; Reis, Agnaldo José da Rocha; Moulin, Luciano Souza; Nascimento, Paulo Cesar do
The importance of Short-Term Load Forecasting (STLF) has been increasing lately. With deregulation and competition, energy price forecasting has become a big business. Bus load forecasting is essential to feed analytical methods utilized for determining energy prices. The variability and non-stationarity of loads are becoming worse due to the dynamics of energy tariffs. Besides, the number of nodal loads to be predicted does not allow frequent interventions from load forecasting experts. More autonomous load predictors are needed in the new competitive scenario. The application of neural network-based STLF has developed sophisticated practical systems over the years. However, the question of how to maximize the generalization ability of such machines, together with the choice of architecture, activation functions, training set data and size, etc. makes up a huge number of possible combinations for the final Neural Network (NN) design, whose optimal solution has not been figured yet. This paper describes a STLF system which uses a non-parametric model based on a linear model coupled with a polynomial network, identified by pruning/growing mechanisms. The load forecaster has special features of data preprocessing and confidence intervals calculations, which are also described. Results of load forecasts are presented for one year with forecasting horizons from 15 min. to 168 hours ahead
Aplicação da transformada wavelet discreta na previsão de carga a curto prazo via redes neurais.
(2004) Reis, Agnaldo José da Rocha; Silva, Alexandre Pinto Alves da
The importance of short-termload forecasting has been in-creasing lately. With deregulation and competition, energy price forecasting has become a big business. Bus-loadfore-castingis essential to feed analytical methods utilized for de- termining energy prices. The variability and non-stationarity of loads are be coming worse due to the dynamics of energy prices. Besides, the number of nodal loads to be predicted does notal low frequent interventions from load forecasting experts. More autonomous load predictors are needed in the new competitive scenario. This paper proposes novel wavelet transform-based technique for short-term load fore-casting via neural networks. Its main goal is to develop more robust load forecasters. Two whole years of load data from a North-American electric utility has been used in order to test The proposed methodology
A hierarchical neural model in short-term load forecasting.
(2004) Carpinteiro, Otávio Augusto Salgado; Reis, Agnaldo José da Rocha; Silva, Alexandre Pinto Alves da
This paper proposes a novel neural model to the problem of short-term load forecasting (STLF). The neural model is made up of two self-organizing map (SOM) nets—one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained on load data extracted from a Brazilian electric utility, and compared to a multilayer perceptron (MLP) load forecaster. It was required to predict once every hour the electric load during the next 24 h. The paper presents the results, the conclusions, and points out some directions for future work.

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