Optimasi Peramalan Beban Listrik dengan Regresi Linear Berganda Berbasis Algoritma Genetika: Studi Kasus Kabupaten Soppeng - Khaidir, Muhammad; Karim, Saktiani; Rahman N, Muhammad Fathur; Mustafa, Syahrul; Virgiawan, Reihan (Jurnal Mosfet, 2025)
Electricity is a crucial infrastructure supporting economic and social development, thus requiring reliable supply planning. In Soppeng Regency, historical data from 2019–2023 show population growth from 226,992 to 240,955 people and GRDP increase from 10,938 to 14,909 billion rupiah, driving higher electricity demand. Customer numbers rose in the household sector from 29,438 to 34,376 and in industry from 33 to 58, with similar upward trends in connected load and energy use. This study addresses the challenge of accurate forecasting by applying Multiple Linear Regression (MLR) optimized with Genetic Algorithm (GA). Forecasting results up to 2028 predict household customers reaching 40,449 and industrial customers 89, while connected load grows from 35.7 to 34.0 million VA in households and from 8.6 to 17.4 million VA in industry. Electricity consumption is projected to exceed 41.8 million kWh in households and 13.5 million kWh in industry by 2028. Model evaluation using Mean Absolute Percentage Error (MAPE) confirms reliable accuracy, making the MLR–GA approach effective for future capacity planning.