Fuyang Jiang
ABSTRACT
Energy-efficient and grid-interactive buildings are a critical enabler for the ongoing energy transition. In recent years, data-driven methods have emerged as a popular option both to improve energy efficiency as well as leverage existing flexibility to offer it as a grid service. However, these models suffer from several shortcomings: (1) their black-box nature and lack of interpretability limits adoption in practice, (2) limited data availability and lack of counterfactuals adversely affects generalization, and (3) data-driven learning of (potentially) non-causal relationships precludes their use in downstream tasks such as active control. In this paper, using two simple case studies, we demonstrate how these issues affect data-driven techniques in practice, and how causal machine learning techniques can help obtain debiased thermal models and predictions, which are both reliable and accurate. Our results show key limitations of commonly utilized methods, the conditions in which they fail, where causal techniques can improve on these existing methods, and how their use can accelerate the exploitation of thermal energy flexibility in buildings.
- Susan Athey, Julie Tibshirani, and Stefan Wager. 2019. Generalized Random Forests. The Annals of Statistics 47, 2 (April 2019). https://doi.org/10.1214/18-AOS1709Google Scholar
Cross Ref
- Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, and Whitney Newey. 2017. Double/Debiased/Neyman Machine Learning of Treatment Effects. American Economic Review 107, 5 (May 2017), 261–265. https://doi.org/10.1257/aer.p20171038Google ScholarCross Ref
- Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. 2017. Double/Debiased Machine Learning for Treatment and Causal Parameters. arxiv:1608.00060 [econ, stat]Google Scholar
- Miguel A Hernan and James M Robins. [n. d.]. Causal Inference: What If. ([n. d.]).Google Scholar
- P. Jayathissa, M. Luzzatto, J. Schmidli, J. Hofer, Z. Nagy, and A. Schlueter. 2017. Optimising Building Net Energy Demand with Dynamic BIPV Shading. Applied Energy 202 (Sept. 2017), 726–735. https://doi.org/10.1016/j.apenergy.2017.05.083Google ScholarCross Ref
- Hussain Kazmi, Chun Fu, and Clayton Miller. 2023. Ten questions concerning data-driven modelling and forecasting of operational energy demand at building and urban scale. Building and Environment 239 (2023), 110407.Google ScholarCross Ref
- Hussain Kazmi, Íngrid Munné-Collado, Fahad Mehmood, Tahir Abbas Syed, and Johan Driesen. 2021. Towards data-driven energy communities: A review of open-source datasets, models and tools. Renewable and Sustainable Energy Reviews 148 (2021), 111290.Google ScholarCross Ref
- Michaela Killian and Martin Kozek. 2016. Ten questions concerning model predictive control for energy efficient buildings. Building and Environment 105 (2016), 403–412.Google ScholarCross Ref
- Sören R. Künzel, Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu. 2019. Metalearners for Estimating Heterogeneous Treatment Effects Using Machine Learning. Proceedings of the National Academy of Sciences 116, 10 (March 2019), 4156–4165. https://doi.org/10.1073/pnas.1804597116Google ScholarCross Ref
- Zoltan Nagy, Gregor Henze, Sourav Dey, Javier Arroyo, Lieve Helsen, Xiangyu Zhang, Bingqing Chen, Kadir Amasyali, Kuldeep Kurte, Ahmed Zamzam, 2023. Ten questions concerning reinforcement learning for building energy management. Building and Environment (2023), 110435.Google Scholar
- Xinkun Nie and Stefan Wager. 2020. Quasi-Oracle Estimation of Heterogeneous Treatment Effects. arxiv:1712.04912 [econ, math, stat]Google Scholar
Index Terms
- On the causality of data-driven building thermal models
Recommendations
A Survey of Learning Causality with Data: Problems and Methods
This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations. In what ways is learning causality in the era of big data different from—or the same as—the traditional one? To answer ...
From dependency to causality: a machine learning approach
The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good ...
Disentangling causality: assumptions in causal discovery and inference
AbstractCausality has been a burgeoning field of research leading to the point where the literature abounds with different components addressing distinct parts of causality. For researchers, it has been increasingly difficult to discern the assumptions ...
Comments