Short-Term Load Forecasting Based On RBF Neural Networks And Fuzzy Control Of Power System

The Short-Term Load Forecasting (STLF) is a daily routine in the operations of power system. Accurate STLF plays an important role for planning, economical scheduling and security analysis in production, which directly influences the profit of the electric utility enterprises. The existing methods for STLF of network are investigated and reviewed in the thesis. According to the rule of change of load characteristic, after calculating the factors such as date type, temperature, weather status etc which influencing the load forecasting a forecasting method based on radial basis function neural networks and fuzzy control. This model speeds rapidly, improves convergence property in training process and the number of neurons in the hidden layer can be significantly decreased, fuzzy control more revises the forecasting error, so the forecasting accuracy can be increased effectively. The implemented program based on the proposed method is used for the STLF in the actual network, the testing results illustrate that the forecasting accuracy is satisfactory, accordingly it shows the validity and practicability of the method.