Unit commitment using load forecasting based on artificial neural networks

This paper consists of two parts. While the first part shows the application of artificial neural networks to load forecasting using new input-output models, the second part utilizes the results from the first part in unit commitment. Based on the forecasts provided, unit commitment schedules are obtained for both hourly and daily load variations. Issues related to both problems are discussed along with an illustration of the two-step method using data obtained from a local utility. While 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 forecasting method. For the chosen inputs, the simulations here show an average error of 4.3% and 3.1% in the case of the daily (twenty-four hours ahead) and hourly (one hour ahead) load forecast, respectively.