Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm |
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| Authors: | Dong-xiao Niu Hui-feng Shi Desheng Dash Wu |
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| Affiliation: | 1. School of Business Administration, North China Electric Power University, Beijing 102206, China;2. School of Mathematics and Physics, North China Electric Power University, Baoding 071003, China;3. RiskLab, University of Toronto, 1 Spadina Crescent, Toronto, ON, Canada M5S 3G3;4. School of Science and Engineering, Reykjavík University, Menntavegur 1, 101 Reykjavík, Iceland;1. TRIUMF, 4004 Wesbrook Mall, Vancouver, BC, Canada V6T2A3;2. Department of Chemical and Biological Engineering, University of British Columbia, 2360 East Mall, Vancouver, BC, Canada V6T1Z3;1. Laboratory of Neurophysiology, Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Eiveniu 4, Kaunas LT 50009, Lithuania;2. Department of Physics, Mathematics and Biophysics, Medical Academy, Lithuanian University of Health Sciences, Eiveniu 4, Kaunas LT 50009, Lithuania;1. Key Laboratory of Materials Physics, Institute of Solid State Physics, Chinese Academy of Sciences, P. O. Box 1129, Hefei 230031, PR China;2. University of Science and Technology of China, Hefei 230026, PR China;3. Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials (Ministry of Education), Shandong University, Jinan, Shandong 250061, PR China;1. College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China;2. Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia |
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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. |
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