Enhanced Short-Term Load Forecasting Using Artificial Neural Networks

The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and development is to render power electronic equipment more controllable, to ensu...

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Autores principales:	Athanasios Ioannis Arvanitidis, Dimitrios Bargiotas, Aspassia Daskalopulu, Vasileios M. Laitsos, Lefteri H. Tsoukalas
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	smart grids load forecasting artificial neural networks data pre-processing Technology T
Acceso en línea:	https://doaj.org/article/b341f2af7818453da3313c55f16d5e36
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spelling	oai:doaj.org-article:b341f2af7818453da3313c55f16d5e362021-11-25T17:28:46ZEnhanced Short-Term Load Forecasting Using Artificial Neural Networks10.3390/en142277881996-1073https://doaj.org/article/b341f2af7818453da3313c55f16d5e362021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7788https://doaj.org/toc/1996-1073The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and development is to render power electronic equipment more controllable, to ensure maximal use of current circuits, system flexibility and efficiency, as well as the relatively easy integration of renewable energy resources at all voltage levels. The current revolution in communication technologies and the Internet of Things (IoT) offers us an opportunity to supervise and regulate the power grid, in order to achieve more reliable, efficient, and cost-effective services. One of the most critical aspects of efficient power system operation is the ability to predict energy load requirements, i.e., load forecasting. Load forecasting is essential for balancing demand and supply and for determining electricity prices. Typically, load forecasting has been supported through the use of Artificial Neural Networks (ANNs), which, once trained on a set of data, can predict future loads. The accuracy of the ANNs’ prediction depends on the quality and availability of the training data. In this paper, we propose novel data pre-processing strategies, which we apply to the data used to train an ANN, and subsequently evaluate the quality of the predictions it produces, to demonstrate the benefits gained. The proposed strategies and the obtained results are illustrated using consumption data from the Greek interconnected power system.Athanasios Ioannis ArvanitidisDimitrios BargiotasAspassia DaskalopuluVasileios M. LaitsosLefteri H. TsoukalasMDPI AGarticlesmart gridsload forecastingartificial neural networksdata pre-processingTechnologyTENEnergies, Vol 14, Iss 7788, p 7788 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	smart grids load forecasting artificial neural networks data pre-processing Technology T
spellingShingle	smart grids load forecasting artificial neural networks data pre-processing Technology T Athanasios Ioannis Arvanitidis Dimitrios Bargiotas Aspassia Daskalopulu Vasileios M. Laitsos Lefteri H. Tsoukalas Enhanced Short-Term Load Forecasting Using Artificial Neural Networks
description	The modernization and optimization of current power systems are the objectives of research and development in the energy sector, which is motivated by the ever-increasing electricity demands. The goal of such research and development is to render power electronic equipment more controllable, to ensure maximal use of current circuits, system flexibility and efficiency, as well as the relatively easy integration of renewable energy resources at all voltage levels. The current revolution in communication technologies and the Internet of Things (IoT) offers us an opportunity to supervise and regulate the power grid, in order to achieve more reliable, efficient, and cost-effective services. One of the most critical aspects of efficient power system operation is the ability to predict energy load requirements, i.e., load forecasting. Load forecasting is essential for balancing demand and supply and for determining electricity prices. Typically, load forecasting has been supported through the use of Artificial Neural Networks (ANNs), which, once trained on a set of data, can predict future loads. The accuracy of the ANNs’ prediction depends on the quality and availability of the training data. In this paper, we propose novel data pre-processing strategies, which we apply to the data used to train an ANN, and subsequently evaluate the quality of the predictions it produces, to demonstrate the benefits gained. The proposed strategies and the obtained results are illustrated using consumption data from the Greek interconnected power system.
format	article
author	Athanasios Ioannis Arvanitidis Dimitrios Bargiotas Aspassia Daskalopulu Vasileios M. Laitsos Lefteri H. Tsoukalas
author_facet	Athanasios Ioannis Arvanitidis Dimitrios Bargiotas Aspassia Daskalopulu Vasileios M. Laitsos Lefteri H. Tsoukalas
author_sort	Athanasios Ioannis Arvanitidis
title	Enhanced Short-Term Load Forecasting Using Artificial Neural Networks
title_short	Enhanced Short-Term Load Forecasting Using Artificial Neural Networks
title_full	Enhanced Short-Term Load Forecasting Using Artificial Neural Networks
title_fullStr	Enhanced Short-Term Load Forecasting Using Artificial Neural Networks
title_full_unstemmed	Enhanced Short-Term Load Forecasting Using Artificial Neural Networks
title_sort	enhanced short-term load forecasting using artificial neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b341f2af7818453da3313c55f16d5e36
work_keys_str_mv	AT athanasiosioannisarvanitidis enhancedshorttermloadforecastingusingartificialneuralnetworks AT dimitriosbargiotas enhancedshorttermloadforecastingusingartificialneuralnetworks AT aspassiadaskalopulu enhancedshorttermloadforecastingusingartificialneuralnetworks AT vasileiosmlaitsos enhancedshorttermloadforecastingusingartificialneuralnetworks AT lefterihtsoukalas enhancedshorttermloadforecastingusingartificialneuralnetworks
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Enhanced Short-Term Load Forecasting Using Artificial Neural Networks

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