Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods

Forecasting renewable energy sources is of critical importance to several practical applications in the energy field. However, due to the inherent volatile nature of these energy sources, doing so remains challenging. Numerous time-series methods have been explored in literature, which consider only...

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Autores principales:	Robert Basmadjian, Amirhossein Shaafieyoun, Sahib Julka
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	time-series auto-regression moving average forecasting models percentage of renewable energy sources Technology T
Acceso en línea:	https://doaj.org/article/1fb8247222b14d11b57ea29ce6873e71
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spelling	oai:doaj.org-article:1fb8247222b14d11b57ea29ce6873e712021-11-11T16:09:17ZDay-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods10.3390/en142174431996-1073https://doaj.org/article/1fb8247222b14d11b57ea29ce6873e712021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7443https://doaj.org/toc/1996-1073Forecasting renewable energy sources is of critical importance to several practical applications in the energy field. However, due to the inherent volatile nature of these energy sources, doing so remains challenging. Numerous time-series methods have been explored in literature, which consider only one specific type of renewables (e.g., solar or wind), and are suited to small-scale (micro-level) deployments. In this paper, the different types of renewable energy sources are reflected, which are distributed at a national level (macro-level). To generate accurate predictions, a methodology is proposed, which consists of two main phases. In the first phase, the most relevant variables having impact on the generation of the renewables are identified using correlation analysis. The second phase consists of (1) estimating model parameters, (2) optimising and reducing the number of generated models, and (3) selecting the best model for the method under study. To this end, the three most-relevant time-series auto-regression based methods of SARIMAX, SARIMA, and ARIMAX are considered. After deriving the best model for each method, then a comparison is carried out between them by taking into account different months of the year. The evaluation results illustrate that our forecasts have mean absolute error rates between 6.76 and 11.57%, while considering both inter- and intra-day scenarios. The best models are implemented in an open-source REN4Kast software platform.Robert BasmadjianAmirhossein ShaafieyounSahib JulkaMDPI AGarticletime-seriesauto-regressionmoving averageforecasting modelspercentage of renewable energy sourcesTechnologyTENEnergies, Vol 14, Iss 7443, p 7443 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	time-series auto-regression moving average forecasting models percentage of renewable energy sources Technology T
spellingShingle	time-series auto-regression moving average forecasting models percentage of renewable energy sources Technology T Robert Basmadjian Amirhossein Shaafieyoun Sahib Julka Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods
description	Forecasting renewable energy sources is of critical importance to several practical applications in the energy field. However, due to the inherent volatile nature of these energy sources, doing so remains challenging. Numerous time-series methods have been explored in literature, which consider only one specific type of renewables (e.g., solar or wind), and are suited to small-scale (micro-level) deployments. In this paper, the different types of renewable energy sources are reflected, which are distributed at a national level (macro-level). To generate accurate predictions, a methodology is proposed, which consists of two main phases. In the first phase, the most relevant variables having impact on the generation of the renewables are identified using correlation analysis. The second phase consists of (1) estimating model parameters, (2) optimising and reducing the number of generated models, and (3) selecting the best model for the method under study. To this end, the three most-relevant time-series auto-regression based methods of SARIMAX, SARIMA, and ARIMAX are considered. After deriving the best model for each method, then a comparison is carried out between them by taking into account different months of the year. The evaluation results illustrate that our forecasts have mean absolute error rates between 6.76 and 11.57%, while considering both inter- and intra-day scenarios. The best models are implemented in an open-source REN4Kast software platform.
format	article
author	Robert Basmadjian Amirhossein Shaafieyoun Sahib Julka
author_facet	Robert Basmadjian Amirhossein Shaafieyoun Sahib Julka
author_sort	Robert Basmadjian
title	Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods
title_short	Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods
title_full	Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods
title_fullStr	Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods
title_full_unstemmed	Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods
title_sort	day-ahead forecasting of the percentage of renewables based on time-series statistical methods
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1fb8247222b14d11b57ea29ce6873e71
work_keys_str_mv	AT robertbasmadjian dayaheadforecastingofthepercentageofrenewablesbasedontimeseriesstatisticalmethods AT amirhosseinshaafieyoun dayaheadforecastingofthepercentageofrenewablesbasedontimeseriesstatisticalmethods AT sahibjulka dayaheadforecastingofthepercentageofrenewablesbasedontimeseriesstatisticalmethods
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Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods

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