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Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network

1
Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
2
Science Engineer Laboratory for Energy, National School of Applied Sciences, Chouaib Doukkali University, El Jadida 24000, Morocco
*
Authors to whom correspondence should be addressed.
Academic Editors: Mohamed Abdel-Nasser, Karar Mahmoud and Sonia Leva
Received: 4 July 2021 / Revised: 28 October 2021 / Accepted: 28 October 2021 / Published: 2 November 2021
(This article belongs to the Special Issue Advanced Forecasting Methods with Applications to Smart Grids)
With an uninterrupted power supply to the consumer, it is obligatory to balance the electricity generated by the electricity load. The effective planning of economic dispatch, reserve requirements, and quality power provision for accurate consumer information concerning the electricity load is needed. The burden on the power system engineers eased electricity load forecasting is essential to ensure the enhanced power system operation and planning for reliable power provision. Fickle nature, atmospheric parameters influence makes electricity load forecasting a very complex and challenging task. This paper proposed a multilayer perceptron neural network (MLPNN) with an association of recursive fine-tuning strategy-based different forecasting horizons model for electricity load forecasting. We consider the atmospheric parameters as the inputs to the proposed model, overcoming the atmospheric effect on electricity load forecasting. Hidden layers and hidden neurons based on performance investigation performed. Analyzed performance of the proposed model with other existing models; the comparative performance investigation reveals that the proposed forecasting model performs rigorous with a minimal evaluation index (mean square error (MSE) of 1.1506 × 10−05 for Dataset 1 and MSE of 4.0142 × 10−07 for Dataset 2 concern to the single hidden layer and MSE of 2.9962 × 10−07 for Dataset 1, and MSE of 1.0425 × 10−08 for Dataset 2 concern to two hidden layers based proposed model) and compared to the considered existing models. The proposed neural network possesses a good forecasting ability because we develop based on various atmospheric parameters as the input variables, which overcomes the variance. It has a generic performance capability for electricity load forecasting. The proposed model is robust and more reliable. View Full-Text
Keywords: multilayer perceptron neural network; electricity load; atmospheric; different forecasting horizons; forecasting multilayer perceptron neural network; electricity load; atmospheric; different forecasting horizons; forecasting
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MDPI and ACS Style

Madhiarasan, M.; Louzazni, M. Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network. Forecasting 2021, 3, 804-838. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3040049

AMA Style

Madhiarasan M, Louzazni M. Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network. Forecasting. 2021; 3(4):804-838. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3040049

Chicago/Turabian Style

Madhiarasan, Manogaran, and Mohamed Louzazni. 2021. "Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network" Forecasting 3, no. 4: 804-838. https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3040049

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