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Short Term Load Forecasting System Using Artificial Neural Networks For Improved Demand Scheduling By Power Utilities.

Article scientifique 2021 Anglais

Résumé

Abstract Load forecasting is a technique used by power utilities to predict electricity demand to maintain the balance between supply and demand. The problem comes when the power utilities draw more than the inadvertent power from the power pool. This necessitates the need for more accurate forecasting models. In this study, a short-term load forecasting system using artificial neural networks in MatLab was performed. The Levenberg-Marquardt optimization technique which has one of the best learning rates was used as a back-propagation algorithm for the Multilayer Feed Forward ANN model using MatLab® R2018a ANN Toolbox. Historical electricity load data obtained from a feeder line at the Zimbabwe Electricity Transmission and Distribution Company (ZETDC) Marvel 420 kV substation in Bulawayo Zimbabwe was used for the training, testing, and validation of the model. Results indicate that ANNs can forecast load with an accuracy of 6.71%. The results indicate that the proposed technique is robust in forecasting future load demands for the daily operational planning of power system distribution

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Sibanda, J., Gwamuri, J. (2021). Short Term Load Forecasting System Using Artificial Neural Networks For Improved Demand Scheduling By Power Utilities.. https://doi.org/10.21203/rs.3.rs-451487/v1

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Jubilee Mzingaye Sibanda Jephias Gwamuri

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