Optimization of Smart Building Electrical Load Prediction Using Long Short-Term Memory

Ali Aqil; Yoga Tri Nugraha; Sumita Wardani; Mawardi; Muhammad Irwanto

doi:10.33019/hn0m4j24

Authors

Ali Aqil Department of Electrical Engineering, Universitas Al-Azhar
Yoga Tri Nugraha Department of Electrical Engineering, Universitas Al-Azhar
Sumita Wardani Department of Electrical Engineering, Universitas Al-Azhar
Mawardi Department of Mechanical Engineering, Universitas Al-Azhar
Muhammad Irwanto Department of Electrical Engineering, Universitas Prima Indonesia

DOI:

https://doi.org/10.33019/hn0m4j24

Keywords:

Smart building, Electrical load prediction, Long Short-Term Memory, Time series, Energy management

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.

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