Comparison of artificial intelligence based techniques for short term load forecasting

Ghanbari, A ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1109/BIFE.2010.12
  3. Publisher: 2010
  4. Abstract:
  5. The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads of Iran by means of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) which are the most successful AI techniques in this field. In order to improve forecasting accuracy, all AI techniques are equipped with preprocessing concept, and effects of this concept on performance of each AI technique are investigated. Finally, outcomes of the approaches are evaluated and compared by means of the mean absolute percentage error (MAPE). Results show that data preprocessing can significantly improve performance of the AI techniques. Meanwhile, ANFIS outcomes are more approximate to the actual loads than those of ANN and GA, so it can be considered as a suitable tool to deal with STLF problems
  6. Keywords:
  7. Adaptive neuro-fuzzy inference system ; AI techniques ; Artificial Neural Network ; Data preprocessing ; Electricity load ; Engineering problems ; Forecasting accuracy ; Load forecasting ; Mean absolute percentage error ; Power systems ; Rate of attraction ; Short term ; Short term load forecasting ; Short-term electrical loads ; Supply and demand ; Supply and demand management ; Time series forecasting ; Artificial intelligence ; Fuzzy inference ; Fuzzy neural networks ; Fuzzy systems ; Genetic algorithms ; Time series ; Electric load forecasting
  8. Source: Proceedings - 3rd International Conference on Business Intelligence and Financial Engineering, BIFE 2010, 13 August 2010 through 15 August 2010 ; 2010 , Pages 6-10 ; 9780769541167 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/5621717