Short-Term Electricity Load Forecasting for Campus Buildings Using LSTM Networks

Budi Santoso

Jurnal Teknologi dan Inovasi, Vol. 1 No. 2 (2026), pp. 9-19

Abstract

Accurate short-term load forecasts help campus facility managers schedule air conditioning and avoid peak-demand charges. This paper evaluates long short-term memory (LSTM) networks for hourly load forecasting across five academic buildings using two years of smart-meter data enriched with calendar and weather features. The LSTM is compared with seasonal ARIMA and gradient-boosted trees over a rolling-origin evaluation. For a 24-hour horizon, the LSTM achieved a mean absolute percentage error of 6.4%, compared with 9.1% for seasonal ARIMA and 7.2% for gradient boosting. Performance dropped during semester breaks, when occupancy patterns change abruptly, which suggests that academic-calendar features are essential. The model has been integrated into a dashboard that alerts operators to expected peaks a day in advance.

Keywords: load forecasting; LSTM; smart campus; time series; energy management

1. Introduction

This section introduces the problem of energy forecasting addressed in this study, reviews the main approaches reported in prior work, and states the research objectives.

2. Methods

We describe the data, the proposed approach, the baselines, and the evaluation protocol used to assess the results summarised in the abstract.

3. Results and Discussion

The results are presented and compared with the baselines. Limitations and practical implications for deployment are discussed.

4. Conclusion

The study shows that the proposed approach is effective for the stated objectives and outlines directions for future work.

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