Neuro-fuzzy approach for short-term electricity price forecasting developed MATLAB-based software

Bid and offer competition is a main transaction approach in deregulated electricity markets. Locational marginal prices (LMP) resulting from bidding competition determine electricity prices at a node or in an area. The LMP exhibits important information for market participants to develop their bidding strategies. Moreover, LMP is also a vital indicator for a Security Coordinator to perform market redispatch for congestion management. This paper presents a method using modular feed forward neural networks (FFNN) and fuzzy inference system (FIS) for forecasting LMPs. FFNN is used to forecast the electricity prices in a short time horizon and FIS to forecast the prices of special days. FFNN system includes an autocorrelation method for selecting parameters and methods for data preprocessing and preparing historical data to train the artificial neural network (ANN). In this paper, the historical LMPs of Pennsylvania, New Jersey, and Maryland (PJM) market are used to test the proposed method. It is found that the proposed neuro-fuzzy method is capable of forecasting LMP values efficiently. In addition, MATLAB-based software is designed to test and use the proposed model in different markets and environments. This is an efficient tool to study and model power markets for price forecasting. It is included with a database management system, data classifier, input variable selection, FFNN and FIS configuration and report generator in custom formats.