Article
Comparison of Baseline Load Forecasting Methodologies for
Active and Reactive Power Demand
Edgar Segovia
1,2,
* , Vladimir Vukovic
2
and Tommaso Bragatto
3
Citation: Segovia, E.; Vukovic, V.;
Bragatto, T. Comparison of Baseline
Load Forecasting Methodologies for
Active and Reactive Power Demand.
Energies 2021, 14, 7533. https://
doi.org/10.3390/en14227533
Academic Editors: Pierluigi Siano,
Hassan Haes Alhelou, Amer Al-Hinai
and Andrzej Bielecki
Received: 5 July 2021
Accepted: 9 November 2021
Published: 11 November 2021
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1
School of Social Sciences, Humanities and Law, Teesside University, Middlesbrough TS1 3BX, UK
2
School of Computing, Engineering and Digital Technologies, Teesside University,
Middlesbrough TS1 3BX, UK; V.Vukovic@tees.ac.uk
3
ASM Terni S.p.A., 05100 Terni, Italy; tommaso.bragatto@asmterni.it
* Correspondence: E.SegoviaLeon@tees.ac.uk
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%).
Keywords:
baseline load forecasting; active and reactive power demand; electricity consumption;
X of Y
1. Introduction
The rise in the quantity and diversity of electronic devices makes linking active and
reactive power demand harder for every customer [
1
]. For such a reason, separate active
and reactive power forecasting is a viable option. Every electrical grid uses a different
approach to predict the electrical consumption. This paper will present popular algorithms
used at present, and compare their accuracy, while at the same time showing the contrast
between the active power and reactive power forecasting. Hence, we try to contribute to
the scientific knowledge of efficient grid management, improving the prediction capacity
of reactive power in the grid which facilitates exploitation of Distributed Energy Resources
(DER) and integration of the renewable energy sources (RES) into the grid [2].
Forecasting methodologies are based on information from a single variable; that is,
forecasting does not need standard deviation calculations that indicate the importance of
each variable (weight) in the objective function, as is the case with regression methods,
parameter estimation, or data reconciliation [
3
]. As such, consistency in calculation of the
errors can be very important for comparing various algorithms [
4
] because it works like
an indicator that is measured consistently. Additionally, coupling this with some sort of
robust filtering like Hampel’s X84 rule [5,6].
Energies 2021, 14, 7533. https://doi.org/10.3390/en14227533 https://www.mdpi.com/journal/energies
