Can Time-Series Foundation Models Perform Building Energy Management Tasks?
🤖 AI Summary
Building energy management (BEM) suffers from scalability bottlenecks due to model customization. This work investigates whether time series foundation models (TSFMs) can alleviate this limitation. Method: We systematically evaluate TSFMs across four tasks—zero-shot univariate forecasting, covariate-augmented thermal modeling, zero-shot representation learning, and robustness—using multi-benchmark comparisons against ARIMA, Prophet, TimesNet, and PatchTST. Contribution/Results: We identify insufficient covariate utilization and weak dynamic contextual modeling as primary causes of TSFMs’ limited generalization. While TSFMs demonstrate zero-shot efficacy in classification-based representation learning, they marginally outperform statistical baselines in forecasting and fail to meaningfully integrate covariates. Performance is highly sensitive to evaluation metrics and building environmental complexity. Overall, current TSFMs lack practical scalability for BEM applications, providing critical diagnostic insights for future architecture design.
Application Category
📝 Abstract
Building energy management (BEM) tasks require processing and learning from a variety of time-series data. Existing solutions rely on bespoke task- and data-specific models to perform these tasks, limiting their broader applicability. Inspired by the transformative success of Large Language Models (LLMs), Time-Series Foundation Models (TSFMs), trained on diverse datasets, have the potential to change this. Were TSFMs to achieve a level of generalizability across tasks and contexts akin to LLMs, they could fundamentally address the scalability challenges pervasive in BEM. To understand where they stand today, we evaluate TSFMs across four dimensions: (1) generalizability in zero-shot univariate forecasting, (2) forecasting with covariates for thermal behavior modeling, (3) zero-shot representation learning for classification tasks, and (4) robustness to performance metrics and varying operational conditions. Our results reveal that TSFMs exhibit emph{limited} generalizability, performing only marginally better than statistical models on unseen datasets and modalities for univariate forecasting. Similarly, inclusion of covariates in TSFMs does not yield performance improvements, and their performance remains inferior to conventional models that utilize covariates. While TSFMs generate effective zero-shot representations for downstream classification tasks, they may remain inferior to statistical models in forecasting when statistical models perform test-time fitting. Moreover, TSFMs forecasting performance is sensitive to evaluation metrics, and they struggle in more complex building environments compared to statistical models. These findings underscore the need for targeted advancements in TSFM design, particularly their handling of covariates and incorporating context and temporal dynamics into prediction mechanisms, to develop more adaptable and scalable solutions for BEM.
Problem
Research questions and friction points this paper is trying to address.
Assessing Time-Series Foundation Models' generalizability in building energy management tasks
Evaluating TSFMs' performance in forecasting and classification compared to statistical models
Identifying limitations in TSFMs' handling of covariates and complex building environments
Innovation
Methods, ideas, or system contributions that make the work stand out.
Time-Series foundation models for diverse BEM tasks
Evaluated generalizability across four key dimensions
Targeted advancements needed for covariates handling
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