Unit Commitment Scheduling by Using the Autoregressive and Artificial Neural Network Models Based Short-Term Load Forecasting

In this study, unit commitment (UC) problem is solved for an optimum schedule of generating units based on the load data forecasted by using artificial neural network (ANN) model and ANN model with autoregressive (AR). Low-cost generation is important in power system analysis. Under forecasting or over forecasting will result in the requirement of purchasing power from spot market or an unnecessary commitment of generating units. Accurate load forecasting is the first step to enhance the UC solution. Lagrange relaxation (LR) method is used for solving the UC problem. Total costs calculated for the actual load and two different forecasting load data are compared. Four-unit Tuncbilek thermal plant which is in Kutahya region, Turkey, is used for this analysis. The data used in this analysis is taken from Turkish Electric Power Company and Electricity Generation Company. All the analyses are implemented using MATLAB^reg.