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Abstract

Load forecasting is an important component for efficient energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly. Besides playing a key role in reducing the generation cost, it is also essential to the reliability of power systems. The system operators use the load forecasting result as a basis of off-line network analysis to determine if the system might be vulnerable. If so, corrective actions should be prepared, such as load shedding, power purchases and bringing peaking units on line. Some data mining algorithms play the greater role to predict the load forecasting. This research work examines and analyzes the use of artificial neural networks (ANN) and genetic algorithm (GA) as forecasting tools for predicting the load demand for three days ahead and comparing the results. Specifically, the ability of neural network (NN) models and genetic algorithm based neural networks (GA-NN) models to predict future electricity load demand is tested by implementing two different techniques such as back propagation algorithm and genetic algorithm based back propagation algorithm (GA-BPN).