Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand

Forecasting the electricity consumption is an essential activity to keep the grid stable and avoid problems in the devices connected to the grid. Equaling consumption to electricity production is crucial in the electricity market. The grids worldwide use different methodologies to predict the demand...

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Autores principales:	Edgar Segovia, Vladimir Vukovic, Tommaso Bragatto
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	baseline load forecasting active and reactive power demand electricity consumption X of Y Technology T
Acceso en línea:	https://doaj.org/article/1ce2f340cdf14360b10d48855b459d04
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spelling	oai:doaj.org-article:1ce2f340cdf14360b10d48855b459d042021-11-25T17:26:21ZComparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand10.3390/en142275331996-1073https://doaj.org/article/1ce2f340cdf14360b10d48855b459d042021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7533https://doaj.org/toc/1996-1073Forecasting the electricity consumption is an essential activity to keep the grid stable and avoid problems in the devices connected to the grid. Equaling consumption to electricity production is crucial in the electricity market. The grids worldwide use different methodologies to predict the demand, in order to keep the grid stable, but is there any difference between making a short time prediction of active power and reactive power into the grid? The current paper analyzes the most usual forecasting algorithms used in the electrical grids: ‘X of Y’, weighted average, comparable day, and regression. The subjects of the study were 36 different buildings in Terni, Italy. The data supplied for Terni buildings was split into active and reactive power demand to the grid. The presented approach gives the possibility to apply the forecasting algorithm in order to predict the active and reactive power and then compare the discrepancy (error) associated with forecasting methodologies. In this paper, we compare the forecasting methodologies using MAPE and CVRMSE. All the algorithms show clear differences between the reactive and active power baseline accuracy. ‘Addition X of Y middle’ and ‘Addition Weighted average’ better follow the pattern of the reactive power demand (the prediction CVRMSE error is between 12.56% and 13.19%) while ‘Multiplication X of Y high’ and ‘Multiplication X of Y middle’ better predict the active power demand (the prediction CVRMSE error is between 12.90% and 15.08%).Edgar SegoviaVladimir VukovicTommaso BragattoMDPI AGarticlebaseline load forecastingactive and reactive power demandelectricity consumptionX of YTechnologyTENEnergies, Vol 14, Iss 7533, p 7533 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	baseline load forecasting active and reactive power demand electricity consumption X of Y Technology T
spellingShingle	baseline load forecasting active and reactive power demand electricity consumption X of Y Technology T Edgar Segovia Vladimir Vukovic Tommaso Bragatto Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand
description	Forecasting the electricity consumption is an essential activity to keep the grid stable and avoid problems in the devices connected to the grid. Equaling consumption to electricity production is crucial in the electricity market. The grids worldwide use different methodologies to predict the demand, in order to keep the grid stable, but is there any difference between making a short time prediction of active power and reactive power into the grid? The current paper analyzes the most usual forecasting algorithms used in the electrical grids: ‘X of Y’, weighted average, comparable day, and regression. The subjects of the study were 36 different buildings in Terni, Italy. The data supplied for Terni buildings was split into active and reactive power demand to the grid. The presented approach gives the possibility to apply the forecasting algorithm in order to predict the active and reactive power and then compare the discrepancy (error) associated with forecasting methodologies. In this paper, we compare the forecasting methodologies using MAPE and CVRMSE. All the algorithms show clear differences between the reactive and active power baseline accuracy. ‘Addition X of Y middle’ and ‘Addition Weighted average’ better follow the pattern of the reactive power demand (the prediction CVRMSE error is between 12.56% and 13.19%) while ‘Multiplication X of Y high’ and ‘Multiplication X of Y middle’ better predict the active power demand (the prediction CVRMSE error is between 12.90% and 15.08%).
format	article
author	Edgar Segovia Vladimir Vukovic Tommaso Bragatto
author_facet	Edgar Segovia Vladimir Vukovic Tommaso Bragatto
author_sort	Edgar Segovia
title	Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand
title_short	Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand
title_full	Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand
title_fullStr	Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand
title_full_unstemmed	Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand
title_sort	comparison of baseline load forecasting methodologies for active and reactive power demand
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1ce2f340cdf14360b10d48855b459d04
work_keys_str_mv	AT edgarsegovia comparisonofbaselineloadforecastingmethodologiesforactiveandreactivepowerdemand AT vladimirvukovic comparisonofbaselineloadforecastingmethodologiesforactiveandreactivepowerdemand AT tommasobragatto comparisonofbaselineloadforecastingmethodologiesforactiveandreactivepowerdemand
_version_	1718412334346534912

Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand

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