A Computing Model of Artificial Intelligent Approaches to Mid-term Load Forecasting: a state-of-the-art- survey for the researcher

 Abstract—This article presents the review of the computing models applied for solving problems of midterm load forecasting. The load forecasting results can be used in electricity generation such as energy reservation and maintenance scheduling. Principle, strategy and results of short term, midterm, and long term load forecasting using statistic methods and artificial intelligence technology (AI) are summaried, Which, comparison between each method and the articles have difference feature input and strategy. The last, will get the idea or literature review conclusion to solve the problem of mid term load forecasting (MTLF).

[1]  Eva Gonzalez-Romera,et al.  Monthly electric energy demand forecasting with neural networks and Fourier series , 2008 .

[2]  Chih-Hsien Kung,et al.  An adaptive power system load forecasting scheme using a genetic algorithm embedded neural network , 1998, IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No.98CH36222).

[3]  Hari Seetha.  Short term electric load prediction using Fuzzy BP , 2007, J. Comput. Inf. Technol..

[4]  Gwo-Ching Liao,et al.  Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting , 2006, IEEE Transactions on Evolutionary Computation.

[5]  Gwo-Ching Liao,et al.  Integrated genetic algorithm/tabu search and neural fuzzy networks for short-term load forecasting , 2004, IEEE Power Engineering Society General Meeting, 2004..

[6]  David Zimbra,et al.  Medium term system load forecasting with a dynamic artificial neural network model , 2006 .

[7]  Albert Y. Zomaya,et al.  Neuro-Adaptive Process Control: A Practical Approach , 1996 .

[8]  A. Prudenzi,et al.  Meteorological Parameters Influence for Medium Term Load Forecasting , 2006, 2005/2006 IEEE/PES Transmission and Distribution Conference and Exhibition.

[9]  Shu Fan,et al.  Electricity peak load forecasting with self‐organizing map and support vector regression , 2006 .

[10]  Ming-Wei Chang,et al.  Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 , 2004, IEEE Transactions on Power Systems.

[11]  Chusak Limsakul,et al.  Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm , 2010, International Conference on Digital Image Processing.

[12]  Ping Wang,et al.  Application of Pattern Recognition and Artificial Neural Network to Load Forecasting in Electric Power System , 2007, Third International Conference on Natural Computation (ICNC 2007).

[13]  Kyung-Bin Song,et al.  Hybrid load forecasting method with analysis of temperature sensitivities , 2006, IEEE Transactions on Power Systems.

[14]  Milde M. S. Lira,et al.  Combining Artificial Neural Networks and Heuristic Rules in a Hybrid Intelligent Load Forecast System , 2006, ICANN.

[15]  C.X. Guo,et al.  A new method for short-term load forecasting integrating fuzzy-rough sets with artificial neural network , 2005, 2005 International Power Engineering Conference.

[16]  A. Prudenzi,et al.  A Knowledge Based System for Medium Term Load Forecasting , 2006, 2005/2006 IEEE/PES Transmission and Distribution Conference and Exhibition.

[17]  Nikos D. Hatziargyriou,et al.  A pattern recognition methodology for evaluation of load profiles and typical days of large electricity customers , 2008 .

[18]  Kostas S. Metaxiotis,et al.  Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher , 2003 .

[19]  Yijia Cao,et al.  Mutual Information and Non-fixed ANNs for Daily Peak Load Forecasting , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[20]  Jian-Chang Lu,et al.  A study of short-term load forecasting based on ARIMA-ANN , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[21]  Gwo-Ching Liao,et al.  Novel GA-based approach and neural fuzzy networks application in short-term load forecasting , 2004, IEEE Power Engineering Society General Meeting, 2004..

[22]  Chusak Limsakul,et al.  Mid Term Load Forecasting of the Country Using Statistical Methodology: Case Study in Thailand , 2009, 2009 International Conference on Signal Processing Systems.

[23]  Magdy M. A. Salama,et al.  A practical approach to electric load forecasting using artificial neural networks with corrective filtering , 1995, Proceedings 1995 Canadian Conference on Electrical and Computer Engineering.

[24]  John V. Ringwood,et al.  Forecasting Electricity Demand on Short, Medium and Long Time Scales Using Neural Networks , 2001, J. Intell. Robotic Syst..

[25]  Danilo Bassi,et al.  Medium Term Electric Load Forecasting Using TLFN Neural Networks , 2006, Int. J. Comput. Commun. Control.

[26]  Milde M. S. Lira,et al.  Using Genetic Algorithm to Develop a Neural-Network-Based Load Forecasting , 2007, ICANN.

[27]  Wang Peng,et al.  Input dimension reduction for load forecasting based on support vector machines , 2004, 2004 IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies. Proceedings.

[28]  T. Yalcinoz,et al.  Short term and medium term power distribution load forecasting by neural networks , 2005 .

[29]  A.C. Liew,et al.  Genetic optimization of a self organizing fuzzy-neural network for load forecasting , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).

[30]  Milde M. S. Lira,et al.  Development of a Hybrid Intelligent System for Electrical Load Forecasting , 2006, IBERAMIA-SBIA.

[31]  E. Georgopoulou,et al.  Models for mid-term electricity demand forecasting incorporating weather influences , 2006 .

[32]  L. L. Lai,et al.  Object-Oriented Genetic Algorithm Based Artificial Neural Network for Load Forecasting , 1998, SEAL.

[33]  Yao Zhang,et al.  The fuzzy logic clustering neural network approach for middle and long term load forecasting , 2007, 2007 IEEE International Conference on Grey Systems and Intelligent Services.

[34]  A. D. Kulkarni,et al.  A neuro-expert system for planning and load forecasting of distribution systems , 1999 .

[35]  Zhao Yang Dong,et al.  Adaptive neural network short term load forecasting with wavelet decompositions , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[36]  H. Mori,et al.  Graphical Modeling for Selecting Input Variables of Short-term Load Forecasting , 2007, 2007 IEEE Lausanne Power Tech.

[37]  Mehmet Kurban,et al.  A New Approach for the Short-Term Load Forecasting with Autoregressive and Artificial Neural Network Models , 2007 .