Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm

Authors:
Dong-xiao Niu;Hui-feng Shi;Desheng Dash Wu
Affiliations:
School of Business Administration, North China Electric Power University, Beijing 102206, China;School of Mathematics and Physics, North China Electric Power University, Baoding 071003, China;RiskLab, University of Toronto, 1 Spadina Crescent, Toronto, ON, Canada M5S 3G3 and School of Science and Engineering, Reykjavík University, Menntavegur 1, 101 Reykjavík, Iceland
Venue:
Applied Soft Computing
Year:
2012

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Abstract

This paper presents a short term load forecasting model based on Bayesian neural network (shorted as BNN) learned by the Hybrid Monte Carlo (shorted as HMC) algorithm. The weight vector parameter of the Bayesian neural network is a multi-dimensional random variable. In learning process, the Bayesian neural network is considered as a special Hamiltonian dynamical system, and the weights vector as the system position variable. The HMC algorithm is used to learn the weight vector parameter with respect to Normal prior distribution and Cauchy prior distribution, respectively. The Bayesian neural networks learned by Laplace algorithm and HMC algorithm and the artificial neural network (ANN) learned by the BP algorithm were used to forecast the hourly load of 25 days of April (Spring), August (Summer), October (Autumn) and January (Winter), respectively. The roots mean squared error (RMSE) and the mean absolute percent errors (MAPE) were used to measured the forecasting performance. The experimental result shows that the BNNs learned by HMC algorithm have far better performance than the BNN learned by Laplace algorithm and the neural network learned BP algorithm and the BNN learned by HMC has powerful generalizing capability, it can welly solve the overfitting problem.