Short Term Load Forecasting using Soft Computing Techniques

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Published Dec 20, 2013
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Volume 9, Issue 4, December 2013

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D. K. Chaturvedi
Professor, Dept. of Electrical EngineeringFaculty of Engineering, D.E.I. Dayalbagh, Agra, U.P.-282 005
A. P. Sinha
Faculty, Dept. of Electrical Engineering, D.E.I. Dayalbagh, Agra, U.P.-282 005
Vikas Pratap Singh
Energy Efficiency and Renewable Energy Division, Central Power Research Institute, Bangalore-560080

Abstract

Soft computing techniques are extensively used for electrical load forecasting in the past such as ANN, Fuzzy Systems, GA etc.. ANN has some limitations, such unknown structure of ANN, Decision of neuron type, problem of training data and time, stuck in local minima etc. To overcome the drawbacks of ANN, a Generalized Neural Network (GNN) has been proposed. In this paper, different variants of GNN have been proposed to improve its performance such as GNN integrated with wavelet transform and trained with adaptive genetic algorithm and fuzzy system to forecast the short term week day electrical load. Performance of the proposed algorithm is compared with other GNN and its other variants on the basis of prediction error.

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How to Cite
Chaturvedi, D. K., Sinha, A. P., & Pratap Singh, V. (2013). Short Term Load Forecasting using Soft Computing Techniques. Power Research - A Journal of CPRI, 9(4), 491–502. Retrieved from https://node6473.myfcloud.com/~geosocin/CPRI/index.php/pr/article/view/856

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