2026-10-11T16:28:27Z https://uji.purwantoro.my.id/oai

oai:uji.purwantoro.my.id:article/3 2026-09-21T13:33:30Z juc:INF

A Hybrid LSTM-Attention Approach to Short-Term Load Forecasting in Island Microgrids Manurung, Yohanes Rahman, Aisha load forecasting LSTM attention microgrid Island microgrids run on diesel and solar, and poor demand forecasts waste fuel. We combine an LSTM encoder with temporal attention and weather covariates to forecast load 24 hours ahead for three microgrids. Mean absolute percentage error drops to 4.8%, against 7.9% for SARIMA and 6.1% for a plain LSTM. Simulated dispatch with the new forecasts reduces diesel consumption by 6.4% per year. Karoteh Utama Publisher 2026-09-21 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://uji.purwantoro.my.id/article/3 JURNAL UJI COBA; Vol. 1 No. 1 (2026); 30-42 1234-273X eng https://uji.purwantoro.my.id/article/3/download/3 Copyright (c) 2026 The Authors https://creativecommons.org/licenses/by/4.0/