A Hybrid LSTM-Attention Approach to Short-Term Load Forecasting in Island Microgrids

Manurung, Yohanes; Rahman, Aisha

Research article · pp. 30-42

A Hybrid LSTM-Attention Approach to Short-Term Load Forecasting in Island Microgrids

Yohanes Manurung¹, Aisha Rahman²

¹ Politeknik Digital Borneo

² Universiti Teknologi Selatan

Received14 May 2026

Revised23 Jul 2026

Accepted22 Aug 2026

Published21 Sep 2026

Abstract

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.

Keywords

load forecasting LSTM attention microgrid

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<meta name="citation_journal_title" content="JURNAL UJI COBA">
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<meta name="citation_issn" content="1234-273X">
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