Application of Chaotic Particle Swarm Optimization in the Short-term Electricity Price Forecasting

The market-oriented reform of the electric power industry is a trend around the world, electricity price issues are the key problems in the power markets and how to price the special commodity-electricity is essential for the smooth market operation. Accurate price forecasting provides crucial information for electricity market participants to make reasonable competing strategies, which is related to the position and benefit of the market participators. So using the relative historic data in predicting the future electricity price is a very meaningful work. With comprehensive considerations of the fluctuation rules and the various influencing factors on the forming of price in the power market, a short-term electricity price forecasting method based on the time series ARMAX model was chosen in this paper. Aimed to solve the problem with traditional method of parameter identification which is easy to fall into the local least values and has low identification precision, chaotic particle swarm optimization (CPSO) algorithm was proposed in this paper. Calculation example shows that this method can reflect the law of the development of the electricity price well and improve forecasting accuracy greatly.