Exploratory Weather Data Analysis for Electricity Load Forecasting Using SVM and GRNN, Case Study in Bali, Indonesia

Siti Aisyah, Arionmaro Asi Simaremare, Didit Adytia, Indra A. Aditya and Andry Alamsyah
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Siti Aisyah: Generation Division, PLN Research Institute, Jakarta 12760, Indonesia
Arionmaro Asi Simaremare: Generation Division, PLN Research Institute, Jakarta 12760, Indonesia
Didit Adytia: School of Computing, Telkom University, Bandung 40257, Indonesia
Indra A. Aditya: Generation Division, PLN Research Institute, Jakarta 12760, Indonesia
Andry Alamsyah: School of Computing, Telkom University, Bandung 40257, Indonesia

Energies, 2022, vol. 15, issue 10, 1-17

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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