Long-term load forecasting by a collaborative fuzzy-neural approach

Abstract

Long-term power load forecasting is of major importance for power suppliers to define the future power consumption of a given region. However, it is not easy to contend with the uncertainty of the long-term load. In order to effectively forecast the long-term load, a collaborative principal component analysis and fuzzy feed-forward neural network (PCA-FFNN) approach is proposed in this study. The difference between this and existing methods is that the collaborative PCA-FFNN approach takes into account the different points of view in a more efficient way, and therefore the results obtained are more comprehensive and more in-depth. In the proposed methodology, a group of domain experts is formed. These domain experts are asked to configure their own PCA-FFNNs to forecast the long-term load based on their views. A collaboration mechanism is therefore established. To facilitate the collaboration process and to derive a single representative value from these forecasts, the partial-consensus fuzzy intersection and radial basis function network (PCFI-RBF) approach is used. The effectiveness of the proposed methodology is illustrated with a case study.