ANN and ANFIS for Short Term Load Forecasting
Load forecasting has become one of the major areas of research in electrical engineering. Short term load forecasting (STLF) is essential for power system planning and economic load dispatch. A variety of mathematical methods has been developed for load forecasting. This paper discusses the influencing factors of STLF and an artificial intelligence (AI) based STLF model for MGVCL load. It also includes comparison of various AI models. Our main objective is to develop the best suited model for MGVCL, by critically evaluating the ways in which the AI techniques proposed are designed and tested.
Keywords:load forecasting, neural network, adaptive neuro fuzzy interface system
K. Kalaitzakis, G. S. Stavrakakis, E. M. Anagnostakis, “Short-term load forecasting based on artificial neural networks parallel implementation”, Electric Power Systems Research, Vol. 63, No. 3, pp. 185-196, 2002 DOI: https://doi.org/10.1016/S0378-7796(02)00123-2
M. Markou, E. Kyriakides, M. Polycarpou “24-Hour Ahead Short Term Load Forecasting Using Multiple MLP’, International Workshop on Deregulated Electricity Market Issues in South-Eastern Europe, pp. 1-6, 2008
E. Banda, K. A. Folly, “Short-Term Load Forecasting Using Artificial Neural Network”, IEEE Lausanne Power Tech, Lausanne, Switzerland, pp. 108-112, July 1-5, 2007 DOI: https://doi.org/10.1109/PCT.2007.4538301
Z. Souzanchi-K, H. Fanaee-T, M. Yaghoubi, M. R. Akbarzadeh-T, “A Multi Adaptive Neuro Fuzzy Inference System for Short Term Load Forecasting by Using Previous Day Features”, International Conference on Electronics and Information Engineering, Kyoto, Japan, Vol. 2, pp. 54-57, August 1-3, 2010 DOI: https://doi.org/10.1109/ICEIE.2010.5559714
H. P. Oak, S. J. Honade, ‘ANFIS Based Short Term Load Forecasting’, International Journal of Current Engineering and Technology, Vol. 5, No.3, pp. 1878-1880, 2015
How to Cite
MetricsAbstract Views: 670
PDF Downloads: 344
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.