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

Nose-Filho, Kenji [UNESP]; Plasencia Lotufo, Anna Diva [UNESP]; Minussi, Carlos Roberto [UNESP]

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

Data

2011-10-01

Autores

Nose-Filho, Kenji

Plasencia Lotufo, Anna Diva

Minussi, Carlos Roberto

Editor

Institute of Electrical and Electronics Engineers (IEEE)

Tipo

Artigo

Direito de acesso

Acesso restrito

Resumo

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.