arXiv cs.AI· Amey Vasulkar, Samuel Kernan Freire, Arie-Willem de Leeuw, Tadeo-Baldiri Salcedo-Rahola·· 6 天前AI 评分20
机器学习与时间序列基础模型用于大学建筑日前电力预测
Machine Learning and Time-Series Foundation Models for Day-Ahead Electricity Forecasting in a University Building
AI 导读
研究对统计模型、机器学习、循环神经网络与预训练 TimesFM 在大学建筑日前电力预测上进行基准测试，TimesFM 3.0 结合协变量取得最低整体误差，平均绝对误差较周季节朴素预测降低约 43%。XGBoost 仍具竞争力，但配对日比较未确立其平均绝对误差优势；TimesFM 无需建筑专属权重训练，XGBoost 则本地拟合与推理更快，LSTM 拟合更慢且未超越两者。
正文 · 原文
Abstract:Day-ahead electricity forecasts can help building operators schedule heating, ventilation, and other services around anticipated demand. Choosing a forecasting model also requires balancing accuracy with its forecast-time inputs, local training, and computational requirements.
We benchmark statistical, machine-learning, recurrent neural-network, and pretrained TimesFM models for a university building. We use chronological validation and evaluate all configurations on the same 57 test days, comparing accuracy across workdays, weekends, and holidays alongside local fitting and forecasting time. Weather covariates use retrospective observations.
TimesFM 3.0 with covariates achieves the lowest overall errors, reducing mean absolute error by approximately 43\% relative to weekly seasonal-naive forecasting. XGBoost remains competitive, although paired daily comparisons do not establish a clear mean-absolute-error advantage for TimesFM. TimesFM also performs strongly using only past electricity consumption. Adding covariates improves its holiday forecasts but slightly worsens workday and weekend forecasts, leaving overall mean absolute error almost unchanged. TimesFM requires no building-specific weight training, while XGBoost offers rapid local fitting and inference. The evaluated LSTM takes longer to fit without outperforming either approach.
An exploratory historical evaluation reveals seasonal variation, while a separate weather-source comparison produces only small changes in the selected TimesFM configuration's accuracy. These results support pretrained TimesFM as a strong initial forecasting baseline for this building, with XGBoost as a competitive locally fitted alternative. Covariates are particularly useful when improved holiday forecasts justify the additional input preparation.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.12638 [cs.LG] |
| (or arXiv:2610.12638v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12638 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Amey Vasulkar [view email]
[v1]
Thu, 8 Oct 2026 19:00:09 UTC (2,871 KB)
来源：arXiv cs.AI · arxiv.org