Citation: Aisyah, S.; Simaremare,
A.A.; Adytia, D.; Aditya, I.A.;
Alamsyah, A. Exploratory Weather
Data Analysis for Electricity Load
Forecasting Using SVM and GRNN,
Case Study in Bali, Indonesia.
Energies 2022, 15, 3566. https://
doi.org/10.3390/en15103566
Academic Editors: Luis
Hernández-Callejo, Sergio
Nesmachnow and Sara Gallardo
Saavedra
Received: 30 March 2022
Accepted: 4 May 2022
Published: 12 May 2022
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Article
Exploratory Weather Data Analysis for Electricity Load Forecasting
Using SVM and GRNN, Case Study in Bali, Indonesia
Siti Aisyah
1
, Arionmaro Asi Simaremare
1
, Didit Adytia
2,
* , Indra A. Aditya
1
and Andry Alamsyah
2
1
Generation Division, PLN Research Institute, Jakarta 12760, Indonesia; siti.aisyah@pln.co.id (S.A.);
arionmaro@pln.co.id (A.A.S.); indra.aditya@pln.co.id (I.A.A.)
2
School of Computing, Telkom University, Bandung 40257, Indonesia; andrya@telkomuniversity.ac.id
* Correspondence: adytia@telkomuniversity.ac.id
Abstract:
Accurate forecasting of electricity load is essential for electricity companies, primarily for
planning electricity generators. Overestimated or underestimated forecasting value may lead to
inefficiency of electricity generator or electricity deficiency in the electricity grid system. Parameters
that may affect electricity demand are the weather conditions at the location of the electricity system.
In this paper, we investigate possible weather parameters that affect electricity load. As a case study,
we choose an area with an isolated electricity system, i.e., Bali Island, in Indonesia. We calculate
correlations of various weather parameters with electricity load in Bali during the period
2018–2019
.
We use two machine learning models to design an electricity load forecasting system, i.e., the
Generalized Regression Neural Network (GRNN) and Support Vector Machine (SVM), using features
from various weather parameters. We design scenarios that add one-by-one weather parameters to
investigate which weather parameters affect the electricity load. The results show that the weather
parameter with the highest correlation value with the electricity load in Bali is the temperature, which
is then followed by sun radiation and wind speed parameter. We obtain the best prediction with
GRNN and SVR with a correlation coefficient value of 0.95 and 0.965, respectively.
Keywords: electricity load; forecasting; weather; GRNN; SVM
1. Introduction
Electricity has become a vital part of the life of modern society nowadays. It is said
that electricity access is an essential factor to enable the economic growth of a country or
region [
1
]. Many studies also imply that the interruption of electricity supply has a severe
impact on business and residential customers [
2
–
4
], where total electricity blackout can
cost up to billions of dollars of economic activity [
5
]. These emphasize the importance of
reliable and stable electricity supply to our current society.
One of the critical tasks in securing the electricity system’s reliability is maintaining the
balance between electricity supply and demand. In current large power systems, the task is
done by adjusting the power generated from generation units in the systems to a forecasted
system electricity demand. Failure to do this correctly may cause the instability of the
power system or even a blackout. On the other hand, low accuracy of electricity demand
forecasting may also cause inefficient and costly operation of the generation units caused by
the requirements of higher capacity of spinning reserve generators and lower efficiency of
thermal generators [
6
]. The latter may also lead to higher carbon emissions which contribute
to global temperature rises or global warming [
7
]. Inevitably, the accuracy of electricity
demand forecasting is paramount in electric power system planning and operation.
There are two approaches for estimating energy use: statistical techniques and artificial
intelligence [
8
]. In recent years, artificial intelligence has accelerated, with one of its appli-
cations being to improve the control of the current generation system. Predicting electrical
loads for energy consumption is no longer a novel concept, as it can be accomplished
Energies 2022, 15, 3566. https://doi.org/10.3390/en15103566 https://www.mdpi.com/journal/energies
