Price forecasting for day-ahead electricity market using Recursive Neural Network

Price forecasting has become a very valuable tool in the current upheaval of electricity market deregulation. It plays an important role in power system planning and operation, risk assessment and other decision making. This paper provides a method for predicting hourly prices in the day-ahead electricity market using Recursive Neural Network (RNN) technique, which is based on similar days approach. RNN is a multi-step approach based on one output node, which uses the previous prediction as input for the subsequent forecasts. In this way, it is carried out recursively for twenty four steps to predict next 24 hour prices. Comparison of forecasting performance of the proposed RNN model with similar days along with other literature is presented. The proposed method is examined on the PJM electricity market. The results obtained through the simulation show that the proposed RNN model can provide efficient, accurate and better results.