Different Methods of Long-Term Electric Load Demand Forecasting a Comprehensive Review

Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost plan. In general, resource planning is performed subject to numerous uncertainties. Expert opinion indicates that a major source of uncertainty in planning for future capacity resource needs and operation of existing generation resources is the forecasted load demand. This paper presents an overview of the past and current practice in long- term demand forecasting. It introduces methods, which consists of some traditional methods, neural networks, genetic algorithms, fuzzy rules, support vector machines, wavelet networks and expert systems.

[1]  K. M. El-Naggar,et al.  Electric Load Forecasting Using Genetic Based Algorithm, Optimal Filter Estimator and Least Error Squares Technique: Comparative Study , 2007 .

[2]  M. Tarafdar Haque,et al.  Application of Neural Networks in Power Systems; A Review , 2007 .

[3]  Ahmet Serdar Yilmaz,et al.  Long Term Energy Consumption Forecasting Using Genetic Programming , 2008 .

[4]  Mostafa Al Mamun,et al.  Artificial neural networks applied to long-term electricity demand forecasting , 2004, Fourth International Conference on Hybrid Intelligent Systems (HIS'04).

[5]  Ping-Feng Pai,et al.  Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms , 2005 .

[6]  M. S. Kandil.  The implementation of long-term forecasting strategies using a knowledge-based expert system: part II , 2002 .

[7]  M. A. Jaramillo,et al.  Electric energy demand forecasting with neural networks , 2002, IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02.

[8]  M. S. Kandil,et al.  The implementation of long-term forecasting strategies using a knowledge-based expert system: part-II , 2001 .

[9]  A. Sangswang,et al.  Using neural network for long term peak load forecasting in Vientiane municipality , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[10]  T.Q.D. Khoa,et al.  Application of wavelet and neural network to long-term load forecasting , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[11]  S. A. Soliman,et al.  Long-term/mid-term electric load forecasting based on short-term correlation and annual growth , 2005 .

[12]  Dahai Zhang,et al.  Power load forecasting algorithm based on wavelet packet analysis , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[13]  M.A. Farahat.  Long-term industrial load forecasting and planning using neural networks technique and fuzzy inference method , 2004, 39th International Universities Power Engineering Conference, 2004. UPEC 2004..

[14]  C. W. Fu,et al.  Models for long-term energy forecasting , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[15]  K. Nagasaka,et al.  Long-term peak demand prediction of 9 Japanese power utilities using radial basis function networks , 2004, IEEE Power Engineering Society General Meeting, 2004..

[16]  A. D. Patton,et al.  Development of an intelligent long-term electric load forecasting system , 1996, Proceedings of International Conference on Intelligent System Application to Power Systems.

[17]  B. Kermanshahi,et al.  Up to year 2020 load forecasting using neural nets , 2002 .