Practical implementation of a hybrid fuzzy neural network for one-day-ahead load forecasting

The paper presents the development and practical implementation of a hybrid short-term electrical load forecasting model for a power system control centre. This hybrid architecture incorporates a Kohonen self-organising feature map with unsupervised learning for classification of daily load patterns, a supervised backpropagation neural network for mapping the temperature/load relationship, and a fuzzy expert system for postprocessing of neural network outputs. This load forecaster requires minimum operator intervention and can be trained adaptively online. The developed model has been tested extensively in the actual operating environment and has been shown to outperform the existing regression-based model