Short Term Load Forecasting Using Curve Fitting Prediction Optimized By Bacterial Foraging Optimization
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Short term load forecast plays an important role in electric power system planning, design and operation. Curve fitting prediction technique and time series models are used for hourly loads forecasting of the week days. In this paper a new approach for short-term load forecasting is introduced. This approach is curve fitting prediction technique combined with Bacterial Foraging optimization. BFO is used to get optimum parameters of the model describing the system with minimum error between actual and forecasted load. The suitability of the proposed approach is illustrated through an application to the actual load data of New England control area.
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