A hybrid metaheuritic technique developed for hourly load forecasting |
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| Authors: | Mohsen Mahrami Rasoul Rahmani Mohammadmehdi Seyedmahmoudian Reza Mashayekhi Hediyeh Karimi Ebrahim Hosseini |
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| Institution: | 1. Department of Computer Engineering, Malard Branch, Islamic Azad University, Malard, Iran;2. School of Software and Electrical Engineering, Faculty of Science Engineering and Technology, Swinburne University of Technology, Melbourne, Victoria, Australia;3. School of Engineering, Deakin University, Waurn Ponds, Victoria, Australia;4. Electrical and Telecommunication Group, Faculty of Electrical Engineering, Khayyam Higher Education Institute, Mashhad, Iran;5. Department of Electronic Systems Engineering, Malaysia‐Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia;6. Information System Department, Faculty of Information and communication Technology, International Islamic University Malaysia, Johor Bahru, Malaysia |
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| Abstract: | Electricity load forecasting has become one of the most functioning tools in energy efficiency and load management and utility companies which has been made very complex due to deregulation. Due to the importance of providing a secure and economic electricty for the consumers, having a reliable and robust enough forecast engine in short‐term load management is very needful. Fuzzy inference system is one of primal branches of Artificial Intelligence techniques which has been widely used for different applications of decision making in complex systems. This paper aims to develop a Fuzzy inference system as a main forecast engine for Short term Load Forecasting (STLF) of a city in Iran. However, the optimization of this platform for this special case remains a basic problem. Hence, to address this issue, the Radial Movement Optimization (RMO) technique is proposed to optimize the whole Fuzzy platform. To support this idea, the accuracy of the proposed model is analyzed using MAPE index and an average error of 1.38% is obtained for the forecast load demand which represents the reliability of the proposed method. Finally, results achieved by this method, demonstrate that an adaptive two‐stage hybrid system consisting of Fuzzy & RMO can be an accurate and robust enough choice for STLF problems. © 2016 Wiley Periodicals, Inc. Complexity 21: 521–532, 2016 |
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| Keywords: | complex forecasting fuzzy inference radial movement optimization electricity demand |
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