Chaotic time series method combined with particle swarm optimization and trend adjustment for electricity demand forecasting |
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| Authors: | Jianzhou Wang Dezhong Chi Jie Wu Hai-yan Lu |
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| Affiliation: | 1. School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China;2. Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia;1. Department of Mechanical Science and Engineering, University of Illinois, Urbana, IL 61801, United States;2. Department of Bioengineering, Stanford University, Stanford, CA 94305, United States;3. Department of Mechanical Engineering, University of Wisconsin at Madison, Madison, WI 53706, United States;4. Department of Aerospace Engineering, University of Illinois, Urbana, IL 61801, United States;1. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA;2. Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA;1. School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China;2. Postdoctoral Research Station of Applied Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, China;3. Institute of Systems Engineering, Macau University of Science and Technology, Taipa Street, Macau;1. Department of Civil Engineering, University of Birmingham, Edgbaston, Birmingham, United Kingdom;2. Hong Kong Observatory, Kowloon, Hong Kong;3. Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong;4. Joint Research Center for Engineering Structure Disaster Prevention and Control, Guangzhou University, China;5. Chongqing University, Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, School of Civil Engineering, Chongqing, China |
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| Abstract: | Electricity demand forecasting plays an important role in electric power systems planning. In this paper, nonlinear time series modeling technique is applied to analyze electricity demand. Firstly, the phase space, which describes the evolution of the behavior of a nonlinear system, is reconstructed using the delay embedding theorem. Secondly, the largest Lyapunov exponent forecasting method (LLEF) is employed to make a prediction of the chaotic time series. In order to overcome the limitation of LLEF, a weighted largest Lyapunov exponent forecasting method (WLLEF) is proposed to improve the prediction accuracy. The particle swarm optimization algorithm (PSO) is used to determine the optimal weight parameters of WLLEF. The trend adjustment technique is used to take into account the seasonal effects in the data set for improving the forecasting precision of WLLEF. A simulation is performed using a data set that was collected from the grid of New South Wales, Australia during May 14–18, 2007. The results show that chaotic characteristics obviously exist in electricity demand series and the proposed prediction model can effectively predict the electricity demand. The mean absolute relative error of the new prediction model is 2.48%, which is lower than the forecasting errors of existing methods. |
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