Forecasting Electricity Demand In Indonesia: Recommendation for Prediction Models to Support PLN’s RUPTL

Zulfatri Aini; Rahmadeni; Tengku Reza Suka Alaqsa

doi:10.56741/IISTR.esl.001696

Authors

Zulfatri Aini
zulfatri_aini@uin-suska.ac.id
Universitas Islam Negeri Sultan Syarif Kasim Riau https://orcid.org/0000-0002-0832-737X
Rahmadeni Universitas Islam Negeri Sultan Syarif Kasim Riau
Tengku Reza Suka Alaqsa Universitas Islam Negeri Sultan Syarif Kasim Riau https://orcid.org/0009-0008-0600-7998

Keywords:

Forecasting, National Recommendation, Neural Network, PLN, RUPTL

Abstract

The Electricity Supply Business Plan (RUPTL) prepared annually by PLN still shows a high error rate in predicting electricity consumption, exceeding 10% in various provinces, such as North Sumatra (36.92%), DKI Jakarta (24.87%), West Kalimantan (40.24%), and South Sulawesi (31.56%), due to the limitations of the linear regression method used in the RUPTL. This study aims to evaluate and recommend the best electricity consumption forecasting model based on artificial intelligence using a Feed Forward Backpropagation Neural Network (FFBP-NN) combined with six training algorithms: Bayesian Regularization (BR), Conjugate Gradient (CG), Levenberg-Marquardt (L-M), Gradient Descent (GD), Quasi-Newton (Q-N), and Resilient Backpropagation (RB), resulting in a total of 13 algorithmic combinations. The data used consists of RUPTL indicators for DKI Jakarta from 2018 to 2023. Testing results of the 13 training functions on the FFBP-NN demonstrate that the TRAINOSS (Quasi-Newton) algorithm achieves the best performance with the lowest Mean Square Error (MSE) of 0.0000065546 and Mean Absolute Percentage Error (MAPE) of 0.06696%. This algorithm outperforms the linear regression method currently used in PLN’s RUPTL, which has a MAPE of approximately 21.14%. The second and third best algorithms are TRAINSCG and TRAINLM, with MAPE values of 0.09455% and 0.10020%, and MSE values of 0.0012160450 and 0.0012229340, respectively. The FFBP-NN model trained with TRAINOSS is highly recommended as the primary alternative to support long-term electricity load planning such as in PLN’s RUPTL.

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Author Biographies

Zulfatri Aini, Universitas Islam Negeri Sultan Syarif Kasim Riau

Associate Professor at the Department of Electrical Engineering, Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia. Her expertise lies in electrical power systems, energy efficiency, and power quality, with extensive experience in teaching, research, and community service since 2006. She has been actively involved in various strategic projects, including integrated energy planning, rural electrification roadmaps, and feasibility studies of renewable energy power plants. Dr. Ir. Aini has published her research in national and international journals indexed by Scopus and authored books in the field of power systems. She has also collaborated with government, industry, and academic institutions, particularly in energy conservation and renewable energy implementation. Her technical competencies include power system analysis using ETAP, EDSA, and LEAP software, and she is a certified trainer in energy management.

Rahmadeni, Universitas Islam Negeri Sultan Syarif Kasim Riau

Statistician and educator with academic foundation in Mathematics (B.Sc., UIN SUSKA Riau) and Statistics (M.Sc., IPB Bogor). Holding the civil service rank of Penata Muda Tk. I/IIIb, she specializes in regression analysis and applied statistics, actively engaging in both research and teaching. Over the years, she has participated in numerous national and international seminars and workshops, including SEMIRATA-ICST (2018), CHELSITECH UMRI (2017), and professional development programs in teaching methodologies and mathematical applications. Her research encompasses diverse topics such as panel data regression, generalized Poisson regression, robust regression, Cox regression models, and mathematical modeling of agricultural products, with publications addressing issues from palm oil production to HIV case studies in Riau. Beyond academia, Rahmadeni contributes to social initiatives, including motivational programs for children and youth, economic empowerment projects using fuzzy linear programming, and mathematics training for primary school teachers. She also serves as an editorial team member for the Journal of Mathematics and Statistics.

Tengku Reza Suka Alaqsa, Universitas Islam Negeri Sultan Syarif Kasim Riau

Received B.Eng degree in Electrical Engineering from Universitas Islam Negeri Sultan Syarif Kasim Riau, in 2025, also recognized as the Top Graduate both at the University and at the Faculty of Science and Technology during the 65th Doctoral, 97th Master’s, and 117th Bachelor’s and Diploma Graduation in 2025. He is also known as STEM Researcher with over 12+ indexed publications in power systems, renewable energy, and forecasting, and associates with institution such as the University of Glasgow, United Kingdom and BRIN, Indonesia. He is also an experienced PV Design Specialist, proficient in tools like Homer Pro, PVSyst, RetScreen, AutoCAD, Helioscope, ETAP, Matlab, and OpenModelica. Reza holds a C1 Advanced English proficiency certification, and has participated in Harvard EdX SEAS Energy & Thermodynamics 2024. He received scholarships from both the Pekanbaru City Government 2023 and Harvard Online Edx, and was selected as a Semifinalist for the 2025 SUSI Global Student Leaders Program by the U.S. Embassy. In leadership, he served as Assistant to the Head of the Electrical Engineering Program 2024–2025. His career interests span across technical engineering roles, business, administration, and marketing. Early in his academic journey, he was also awarded 1st Winner and 2nd Runner-Up in the Riau Provincial Civic Education Olympiad 2017 & 2016.

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