A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation |
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| Authors: | A Azadeh M Saberi A Gitiforouz Z Saberi |
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| Affiliation: | 1. Department of Industrial Engineering and Center of Excellence for Intelligent Based Experimental Mechanics, College of Engineering, University of Tehran, Iran;2. Department of Industrial Engineering, University of Tafresh, Iran;3. Department of Industrial Engineering, Sharif University of Technology, Iran;1. Biophysics laboratory LTIM-LR12ES06, faculty of medicine of Monastir, university of Monastir, 5019 Monastir, Tunisia;2. CRISTAL laboratory, ENSI, research group in forms and images of Tunisia (GRIFT), university of Manouba, 2010 Manouba, Tunisia;1. Chair for Process Control Systems Engineering, Dresden University of Technology, Dresden, Germany;2. Getron Corp., 221 River Street, Hoboken, New Jersey 07030, USA;1. Center of Excellence for Occupational Health Engineering, Research Center for Health Sciences, Faculty of Health, Hamadan University of Medical Sciences, Hamadan, Iran;2. School of Industrial and Systems Engineering, Center of Excellence for Intelligent-Based Experimental Mechanic, College of Engineering, University of Tehran, Iran;3. Safety and Security Science Section, Delft University of Technology, Delft, The Netherlands |
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| Abstract: | This paper presents a hybrid adaptive network based fuzzy inference system (ANFIS), computer simulation and time series algorithm to estimate and predict electricity consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and complex estimation and forecasting. Computer simulation is developed to generate random variables for monthly electricity consumption. Various structures of ANFIS are examined and the preferred model is selected for estimation by the proposed algorithm. Finally, the preferred ANFIS and time series models are selected by Granger–Newbold test. Monthly electricity consumption in Iran from 1995 to 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with genetic algorithm (GA) and artificial neural network (ANN). This is the first study that uses a hybrid ANFIS computer simulation for improvement of electricity consumption estimation. |
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