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

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Abstract

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%).
Original languageEnglish
Article number7533
Number of pages14
JournalEnergies
Volume14
Issue number22
DOIs
Publication statusPublished - 11 Nov 2021

Bibliographical note

Funding Information:
Funding: This research was partly funded by EU’s Horizon 2020 framework programme for research and innovation under grant agreement No 774478, project eDREAM—enabling new Demand REsponse Advanced, Market oriented and secure technologies, solutions and business models.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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