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A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation

Azadeh, A ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1016/j.eswa.2009.02.081
  3. Publisher: 2009
  4. Abstract:
  5. 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. © 2009 Elsevier Ltd. All rights reserved
  6. Keywords:
  7. Computer simulation ; Adaptive network based fuzzy inference system ; Artificial neural network ; Electricity consumption ; Hybrid ; Hybrid approach ; Hybrid simulation ; Improvement ; Modeling approach ; Time series algorithms ; Time series models ; Algorithms ; Backpropagation ; Computational methods ; Computer networks ; Electric power utilization ; Electricity ; Estimation ; Fuzzy inference ; Fuzzy systems ; Model structures ; Neural networks ; Random variables ; Simulators ; Time series ; Electric load forecasting
  8. Source: Expert Systems with Applications ; Volume 36, Issue 8 , 2009 , Pages 11108-11117 ; 09574174 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0957417409002413