A new formulation of multinodal short-term load forecasting based on adaptive resonance theory with reverse training
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Data
2020-02-01
Autores
Amorim, Aline J. [UNESP]
Abreu, Thays A. [UNESP]
Tonelli-Neto, Mauro S. [UNESP]
Minussi, Carlos R. [UNESP]
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Resumo
A multinodal intelligent predictive method for electrical power systems has been developed. Knowing the electrical load accurately and in advance is essential for conducting studies in regard to the system operations, and to create strategies that improve the quality of the energy-supply for commercial, industrial, and residential consumers. The proposed method employs a supervised Fuzzy-ARTMAP neural network, using the new concept of reverse training, to forecast the global demand and load of several nodes of an electric network (multinodal load forecasting) up to 24 h ahead. To evaluate and test the proposed system, an application is presented that considers real historical data from a company in the electric sector. Results show that the reverse training reduces the error of the neural network, making the forecast more accurate, reliable, and very fast.
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Palavras-chave
Adaptive resonance theory, Artificial neural networks, Electrical power systems, Multinodal load forecasting
Como citar
Electric Power Systems Research, v. 179.