Load forecasting is an important component for energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions including decisions on purchasing and generating electric power, load switching, and infrastructure development. Besides playing a key role in reducing the generation cost, it is also essential to the reliability of power systems. Short-term load forecasting (STLF) can help to estimate load flows and to make decisions that can prevent overloading. Timely implementations of such decisions lead to the improvement of network reliability and to the reduced occurrences of equipment failures and blackouts. Load forecasting is also important for contract evaluations and evaluations of various sophisticated financial products on energy pricing offered by the market. In the deregulated economy, decisions on capital expenditures based on long-term forecasting are also more important than in a non-deregulated economy where a rate increase could be justified by capital expenditure projects. Data mining plays the key role to infer the information which is important to make the right decision. In this article we examine and analyze the use of genetic algorithm (GA) techniques for the determination of weights in a back propagation network (BPN) for short-term load forecasting.