A Patch-Based Transformer for Building Energy Load Forecasting
Öz
A considerable portion of worldwide energy usage and carbon emissions comes from buildings. Accurate short-term electricity load forecasting is critical to optimize building energy management systems. In this study, the PatchTST model was evaluated for short-term electricity load forecasting at the building scale. This forecasting problem was addressed using 50 buildings from the public Building Data Genome Project 2 dataset. Experiments were conducted using a strict temporal split strategy, where models generated 24-hour forward predictions from 336 hours of historical data. Temporal features, including hour, day, month, and weekend indicators, were integrated into the PatchTST model. The model was compared against Persistence, Long Short-Term Memory (LSTM), and DLinear baselines. On the test set, the PatchTST model outperformed all baselines, achieving a mean absolute error (MAE) of 7.24 kWh, a root mean square error (RMSE) of 13.85 kWh, an R² of 0.9894, and a symmetric mean absolute percentage error (MAPE) of 9.42%. Per-horizon analyses showed that PatchTST maintained stable performance across the forecast horizon. A building-level Wilcoxon signed-rank test indicated that the superiority of PatchTST over all baselines was statistically significant (p < 0.001) with large effect sizes These findings suggest that patch-based Transformer architectures provide high accuracy, statistical reliability for short-term electricity load forecasting.
Anahtar Kelimeler
Building energy forecasting, PatchTST, Short-term load forecasting, Time series, Transformer
Kaynakça
- Wenninger, S., Kaymakci, C., & Wiethe, C. (2022). Explainable long-term building energy consumption prediction using qlattice. Applied Energy, 308, 118300.
- Lu, C., Li, S., & Lu, Z. (2022). Building energy prediction using artificial neural networks: a literature survey. Energy and Buildings, 262, 111718.
- Olu-Ajayi, R., Alaka, H., Sulaimon, I., Sunmola, F., & Ajayi, S. (2022). Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques. Journal of Building Engineering, 45, 103406.
- Afzal, S., Ziapour, B. M., Shokri, A., Shakibi, H., & Sobhani, B. (2023). Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms. Energy, 282, 128446.
- Zheng, P., Zhou, H., Liu, J., & Nakanishi, Y. (2023). Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture. Applied Energy, 349, 121607.
- Saleh, H. (2025). Wind power prediction based on wind velocity variable and polynomial regression method. El-Cezeri Fen ve Mühendislik Dergisi, 12(3), 274–282.
- Amasyali, K., & El-Gohary, N. M. (2018). A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews, 81, 1192–1205.
- Öter, A., & Ersöz, B. (2025). Artificial intelligence assisted solar energy forecasting by explainability approaches with lime and shap. El-Cezeri Fen ve Mühendislik Dergisi, 12(2), 205–212.
- Çelebi, S. B., Aslan, A., & Yazicioglu, C. (2026). Short-term wind power forecasting using knn, svr, and linear regression with high-resolution scada data. Wind Engineering, 0309524X261417206.
- Kardeş, Z., & Kandemir, T. (2025). Audit opinion prediction with data mining methods: evidence from borsa istanbul (bist). El-Cezeri Fen ve Mühendislik Dergisi, 12(3), 395–409.