Day-Ahead Price Forecasting of Electricity Market Using Neural Networks and Wavelet Transform

In a competitive environment, participants chooses their bid with regard to policy their advantages and market conditions. Therefore one of the essential and necessary discussions in competitive environment is prices prediction. In this paper artificial neural network method is used for load prediction by considering the most maximum impact factors in prediction and most influencing data as input of model. Proposed model is experienced on Nord Pool electricity market and the results are checked in various stages. Also for expression of system error, an indicator of MAPE has been used. This error provides a good indication of the constraints and applicability of these predictions. To reduce the size of the input data and obtain better results, a filter been used to separate parameters with similar frequency. In addition to the electrical loads, the daily temperature has been used as a factor for forecasting to achieve better results. The MAPE obtained from the load forecasting results confirm that the proposed technique is robust in forecasting future load demands and provides reliable forecasts for the daily operational planning of Nord Pool market.