UNIT COMMITMENT USING LOAD FORECASTING BASED ON ARTIFICIAL NEURAL NETWORKS

This paper consists of two parts. While the first part shows the appli cation of artificial neural networks to load forecasting using new inp ut-output models, the second part utilizes the results from the first part in unit commitment. Based on the forecasts provided, unit commitm ent schedules are obtained for both hourly and daily load variations. Issues related to both problems are discussed along with an illustrati on of the two-step method using data obtained from a local utility. Wh ile a generation schedule such as this is not only invaluable to power system planners and operators, it is shown that this two-step process paves the way for an artificial intelligence (AI) type of method for the unit commitment problem based on the same inputs as the load forec asting method. For the chosen inputs, the simulations here show an ave rage error of 4.3% and 3.1% in the case of the daily (twenty-four hour s ahead) and hourly (one hour ahead) load forecast, respectively.