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An Effect of Machine Learning Techniques in Electrical Load forecasting and Optimization of Renewable Energy Sources

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Abstract

The prediction of the load from a day ahead or a week ahead is called short-term load forecasting. STLF using ANN gives better results in the power grid because the construction of the model is precise, implementation is easy and the performances are good. The weight consisted neural model is a good whose optimal value was found by using various optimization techniques. This paper explains the effect of different machine learning techniques like genetic algorithm, particle swarm optimization, autoregressive integrated moving average, empirical mode decomposition-particle swarm optimization-adaptive network-based fuzzy inference system in STLF and fuzzy logy for the optimization of renewable energy sources, i.e. solar and wind which is also used for the training of the artificial neural network with the silent effect of backpropagation. The study of different machine learning techniques presented their ability to work to produce the results and their extended application in STLF. From the simulation results, we got an empirical mode decomposition-particle swarm optimization-adaptive network-based fuzzy interference system that provides minor error, which is very much permissible compared to other techniques.

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  1. Department of Electrical Engineering, Veer Surendra Sai University of Technology, Burla, India

    Saroj Kumar Panda & Papia Ray

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  1. Saroj Kumar Panda
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  2. Papia Ray
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Correspondence to Papia Ray.

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Panda, S.K., Ray, P. An Effect of Machine Learning Techniques in Electrical Load forecasting and Optimization of Renewable Energy Sources. J. Inst. Eng. India Ser. B 103, 721–736 (2022). https://doi.org/10.1007/s40031-021-00688-1

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  • DOI: https://doi.org/10.1007/s40031-021-00688-1

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