Abstract
Building modeling, specifically heating, ventilation, and air conditioning (HVAC) load and equivalent energy storage calculations, represent a key focus for decarbonization of buildings and smart grid controls. In this paper, an ultra-fast one-minute resolution Hybrid Machine Learning Model (HMLM) is proposed as part of a novel contribution in the field of residential physics-based smart home surrogate modeling. Emulation of white box models, or digital twins, with editable parameters through machine learning (ML) meta-modeling serves as an alternative to wide-spread experimental Big Data collection. The HMLM employs combined k-means clustering with multiple linear regression (MLR) to emulate minutely HVAC power timestep-to-timestep with satisfactory nRMSE error of less than 10% across an entire year test set. An approach is also described to characterize HVAC systems as generalized storage (GES) devices to unify household appliance and virtual power plant (VPP) controls in accordance with industry Communication Technology Association (CTA) 2045 protocol and Energy Star metrics. Synthetic output data from experimentally calibrated EnergyPlus models for three existing smart homes managed by the Tennessee Valley Authority (TVA) is employed in residential case studies and a discussion provided for the large-scale application to hundreds of homes.
| Original language | English |
|---|---|
| Pages (from-to) | 572-582 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Industry Applications |
| Volume | 61 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2025 |
Bibliographical note
Publisher Copyright:© 1972-2012 IEEE.
Funding
This work was supported in part by National Science Foundation (NSF) under Award 1936131, in part by NSF Graduate Research Fellowship under Grant 2239063, and in part by the Department of Education (DoEd) GAANN Fellowship. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF and DoEd. ACKNOWLEDGMENT This paper is based upon work supported by the National Science Foundation (NSF) under Award No. 1936131 and under NSF Graduate Research Fellowship Grant No. 2239063. The support received through a Department of Education (DoEd) GAANN Fellowship is also gratefully acknowledged. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF and DoEd.
| Funders | Funder number |
|---|---|
| U.S. Department of Education, OSERS | |
| U.S. Department of Energy Chinese Academy of Sciences Guangzhou Municipal Science and Technology Project Oak Ridge National Laboratory Extreme Science and Engineering Discovery Environment National Science Foundation National Energy Research Scientific Computing Center National Natural Science Foundation of China | 2239063, 1936131 |
| U.S. Department of Energy Chinese Academy of Sciences Guangzhou Municipal Science and Technology Project Oak Ridge National Laboratory Extreme Science and Engineering Discovery Environment National Science Foundation National Energy Research Scientific Computing Center National Natural Science Foundation of China |
Keywords
- ANSI/CTA-2045-B
- Heating ventilation and air conditioning (HVAC)
- demand response (DR)
- energy star
- energy take
- general energy storage (GES)
- home energy management (HEM)
- machine learning (ML)
- smart grid
- smart homes
- surrogate model
ASJC Scopus subject areas
- Control and Systems Engineering
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering
