A case study on a hybrid wind speed forecasting method using BP neural network |
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| Authors: | Zhen-hai Guo Jie Wu Hai-yan Lu Jian-zhou Wang |
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| Affiliation: | 1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;2. School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China;3. Faculty of Engineering and Information Technology, University of Technology Sydney, Australia;1. Key Laboratory for Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, Hunan, China;2. Institute of Automation, Faculty of Informatics and Electrical Engineering, University of Rostock, Rostock 18119, Mecklenburg-Vorpommern, Germany;1. Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China;2. Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Lanzhou 730000, China;3. School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China;1. Key Laboratory of Smart Grid of Ministry of Education (Tianjin University), Tianjin 300072, China;2. Hulunbuir University, Hailaer 021008, China;3. Electrical and Computer Engineering Department, Clarkson University, Potsdam, NY 13699, USA;1. Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China;2. College of Science, Nanjing University of Posts and Telecommunications, Nanjing 210046, China;3. Key Laboratory for Land Surface Process and Climate Change in Cold and Arid Regions, Chinese Academy of Sciences, Lanzhou 730000, China;4. School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China |
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| Abstract: | Wind energy, which is intermittent by nature, can have a significant impact on power grid security, power system operation, and market economics, especially in areas with a high level of wind power penetration. Wind speed forecasting has been a vital part of wind farm planning and the operational planning of power grids with the aim of reducing greenhouse gas emissions. Improving the accuracy of wind speed forecasting algorithms has significant technological and economic impacts on these activities, and significant research efforts have addressed this aim recently. However, there is no single best forecasting algorithm that can be applied to any wind farm due to the fact that wind speed patterns can be very different between wind farms and are usually influenced by many factors that are location-specific and difficult to control. In this paper, we propose a new hybrid wind speed forecasting method based on a back-propagation (BP) neural network and the idea of eliminating seasonal effects from actual wind speed datasets using seasonal exponential adjustment. This method can forecast the daily average wind speed one year ahead with lower mean absolute errors compared to figures obtained without adjustment, as demonstrated by a case study conducted using a wind speed dataset collected from the Minqin area in China from 2001 to 2006. |
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