Electrical Load Demand Forecasting Using Feed-Forward Neural Networks

The higher share of renewable energy sources in the electrical grid and the electrification of significant sectors, such as transport and heating, are imposing a tremendous challenge on the operation of the energy system due to the increase in the complexity, variability and uncertainties associated...

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Autores principales:	Eduardo Machado, Tiago Pinto, Vanessa Guedes, Hugo Morais
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	error correction load demand forecast feed-forward neural network Technology T
Acceso en línea:	https://doaj.org/article/31156735011d4c48864b9b9d0efbd439
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id	oai:doaj.org-article:31156735011d4c48864b9b9d0efbd439
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spelling	oai:doaj.org-article:31156735011d4c48864b9b9d0efbd4392021-11-25T17:27:30ZElectrical Load Demand Forecasting Using Feed-Forward Neural Networks10.3390/en142276441996-1073https://doaj.org/article/31156735011d4c48864b9b9d0efbd4392021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7644https://doaj.org/toc/1996-1073The higher share of renewable energy sources in the electrical grid and the electrification of significant sectors, such as transport and heating, are imposing a tremendous challenge on the operation of the energy system due to the increase in the complexity, variability and uncertainties associated with these changes. The recent advances of computational technologies and the ever-growing data availability allowed the development of sophisticated and efficient algorithms that can process information at a very fast pace. In this sense, the use of machine learning models has been gaining increased attention from the electricity sector as it can provide accurate forecasts of system behaviour from energy generation to consumption, helping all the stakeholders to optimize their activities. This work develops and proposes a methodology to enhance load demand forecasts using a machine learning model, namely a feed-forward neural network (FFNN), by incorporating an error correction step that involves the prediction of the initial forecast errors by another FFNN. The results showed that the proposed methodology was able to significantly improve the quality of load demand forecasts, demonstrating a better performance than the benchmark models.Eduardo MachadoTiago PintoVanessa GuedesHugo MoraisMDPI AGarticleerror correctionload demand forecastfeed-forward neural networkTechnologyTENEnergies, Vol 14, Iss 7644, p 7644 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	error correction load demand forecast feed-forward neural network Technology T
spellingShingle	error correction load demand forecast feed-forward neural network Technology T Eduardo Machado Tiago Pinto Vanessa Guedes Hugo Morais Electrical Load Demand Forecasting Using Feed-Forward Neural Networks
description	The higher share of renewable energy sources in the electrical grid and the electrification of significant sectors, such as transport and heating, are imposing a tremendous challenge on the operation of the energy system due to the increase in the complexity, variability and uncertainties associated with these changes. The recent advances of computational technologies and the ever-growing data availability allowed the development of sophisticated and efficient algorithms that can process information at a very fast pace. In this sense, the use of machine learning models has been gaining increased attention from the electricity sector as it can provide accurate forecasts of system behaviour from energy generation to consumption, helping all the stakeholders to optimize their activities. This work develops and proposes a methodology to enhance load demand forecasts using a machine learning model, namely a feed-forward neural network (FFNN), by incorporating an error correction step that involves the prediction of the initial forecast errors by another FFNN. The results showed that the proposed methodology was able to significantly improve the quality of load demand forecasts, demonstrating a better performance than the benchmark models.
format	article
author	Eduardo Machado Tiago Pinto Vanessa Guedes Hugo Morais
author_facet	Eduardo Machado Tiago Pinto Vanessa Guedes Hugo Morais
author_sort	Eduardo Machado
title	Electrical Load Demand Forecasting Using Feed-Forward Neural Networks
title_short	Electrical Load Demand Forecasting Using Feed-Forward Neural Networks
title_full	Electrical Load Demand Forecasting Using Feed-Forward Neural Networks
title_fullStr	Electrical Load Demand Forecasting Using Feed-Forward Neural Networks
title_full_unstemmed	Electrical Load Demand Forecasting Using Feed-Forward Neural Networks
title_sort	electrical load demand forecasting using feed-forward neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/31156735011d4c48864b9b9d0efbd439
work_keys_str_mv	AT eduardomachado electricalloaddemandforecastingusingfeedforwardneuralnetworks AT tiagopinto electricalloaddemandforecastingusingfeedforwardneuralnetworks AT vanessaguedes electricalloaddemandforecastingusingfeedforwardneuralnetworks AT hugomorais electricalloaddemandforecastingusingfeedforwardneuralnetworks
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Electrical Load Demand Forecasting Using Feed-Forward Neural Networks

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