Coupling PSO-GPR Based Medium and Long Term Load Forecasting in Beijing

Abstract

Establishing a scientific and reasonable mid- and long-term power load forecasting method is the premise of power industry planning and construction. This paper constructs a hybrid electric load forecasting model based on Gaussian process (GPR) and particle swarm optimization (PSO). The paper uses the PSO algorithm to optimize the parameters in the co-variance function, and uses the modified parameters as the initial value to train the power load in the GPR model. Under the Bayesian framework, the parameters in the co-variance function are again optimized. Finally, the trained GPR model is used to predict the power load, and the results are compared with the auto-regressive integral moving average model and the exponential smoothing model. The verification results show that the hybrid electric load forecasting model based on Gaussian process (GPR) and particle swarm optimization (PSO) has good stability and higher prediction accuracy, and is suitable for medium and long-term electric load forecasting.