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All Journal Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering)
Ali Aqil
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Optimization of Smart Building Electrical Load Prediction Using Long Short-Term Memory Ali Aqil; Nugraha, Yoga Tri; Sumita Wardani; Mawardi; Muhammad Irwanto
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 13 No 1 (2026): Jurnal Ecotipe, April 2026
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/hn0m4j24

Abstract

The advancement of smart building technologies requires energy management systems that are both efficient and capable of adapting to dynamic operational conditions. A key component of such systems is reliable electrical load forecasting, as building energy demand is affected by environmental conditions, occupancy behavior, and operational activities that exhibit nonlinear and time-dependent characteristics. This study explores the use of the Long Short-Term Memory (LSTM) approach for forecasting smart building electricity consumption based on multivariate time-series data. The input dataset incorporates temporal features, ambient temperature, humidity levels, occupancy-related patterns, and major electrical load components within the building. The research workflow consists of data preprocessing, normalization, time-series construction using a sliding window strategy, LSTM model training, and evaluation of forecasting performance. The findings indicate that the building’s electricity demand varies approximately between 6 kW and 17 kW, with an average load ranging from 11 to 12 kW. Performance assessment yields an RMSE of about 3 kW and a MAPE of roughly 25%. The largely symmetric error distribution around zero suggests minimal systematic bias in the predictions, although the model’s accuracy during peak demand periods remains limited.
Co-Authors Mawardi Muhammad Irwanto Sumita Wardani Yoga Tri Nugraha