LAUSR

Under restructuring of electric power industry and changing traditional vertically integrated electric utility structure to competitive, market clearing price (MCP) prediction models are essential for all generation company (GenCos) for their survival under new deregulated environment. In this paper, a hybrid model is presented to predict hourly electricity MCP. The model contains a Neural Network (NN), Particle swarm optimization (PSO) and Genetic Algorithm (GA). The NN is used as the major forecasting module to predict the electricity MCP values and PSO applied to improve the traditional neural network learning capability and optimizing the weights of the NN and GA applied to optimize NN architecture. The main contribution includes: presenting a hybrid intelligent model for MCP prediction; applying K-Means algorithm to clustering NN’s test set and seasonality pattern detection; and evaluation of its performance by improved MAE with penalty factor for positive error. It has been tested on Iranian real-world electricity market for the one month of the year 2010-2013 that result shown average weighted MAE for day ahead MCP prediction is equal to 0.12 and forecasting of MCP can be improved by more than 6.7% and 4%in MAE in compare of simple NN and combination of NN and bat algorithm.