Comparison of Artificial Intelligence based Techniques for Short Term Load Forecasting
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.
Artificial Intelligence Data Preprocessing Time Series Forecasting Supply and Demand Management
Arash Ghanbari Esmaeil Hadavandi Salman Abbasian-Naghneh
Department of Industrial Engineering University of Tehran Tehran,Iran P.O.Box:11155-4563 Department of Industrial Engineering Sharif University of Technology Tehran,Iran,P.O.Box:11365-9466 Department of Mathematic Islamic Azad University Najafabad Branch,Najafabad,Iran
国际会议
香港
英文
6-10
2010-08-17（万方平台首次上网日期，不代表论文的发表时间）