Digital Twin for HVAC Load and Energy Storage based on a Hybrid ML Model with CTA-2045 Controls Capability

Rosemary E. Alden; Evan S. Jones; Steven B. Poore; Huangjie Gong; Abdullah Al Hadi; Dan M. Ionel

doi:10.1109/ECCE50734.2022.9948141

Digital Twin for HVAC Load and Energy Storage based on a Hybrid ML Model with CTA-2045 Controls Capability

Rosemary E. Alden
, Evan S. Jones
, Steven B. Poore
, Huangjie Gong
, Abdullah Al Hadi
, Dan M. Ionel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

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. Widely used white box models, due to their complexity, are too computationally intensive to be employed in high resolution distributed energy resources (DER) platforms without simulation time delays. In this paper, an ultra-fast one-minute resolution Hybrid Machine Learning Model (HMLM) is proposed as part of a novel procedure to replicate white box models as an alternative to wide spread experimental big data collection. Synthetic output data from experimentally calibrated EnergyPlus models for three existing smart homes managed by the Tennessee Valley Authority is used. The HMLM employs combined k-means clustering and multiple linear regression (MLR) models to predict minutely HVAC power with satisfactory nRMSE error of less than 10% across an entire year test set. An approach is provided to characterize HVAC systems through the newly proposed hybrid model as a generalized storage (GES) device suitable for DER control and event types in accordance with the Communication Technology Association (CTA) 2045 standard and Energy Star metrics such as “energy take”, currently developed by industry, to unify household appliance controls.

Original language	English
Title of host publication	2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022
ISBN (Electronic)	9781728193878
DOIs	https://doi.org/10.1109/ECCE50734.2022.9948141
State	Published - 2022
Event	2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022 - Detroit, United States Duration: Oct 9 2022 → Oct 13 2022

Publication series

Name	2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022

Conference

Conference	2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022
Country/Territory	United States
City	Detroit
Period	10/9/22 → 10/13/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Funding

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. 1839289. 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. REFERENCES

Funders	Funder number
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	1936131, 1839289
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
U.S. Department of Education, OSERS

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

ANSI/CTA-2045-B
Battery Energy Storage System (BESS)
Demand Response (DR)
Energy Star
Energy Storage
Energy Take
Heating Ventilation and Air Conditioning (HVAC)
Home Energy Management (HEM)
machine learning
smart grid
smart homes

ASJC Scopus subject areas

Electrical and Electronic Engineering
Mechanical Engineering
Safety, Risk, Reliability and Quality
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Control and Optimization

Access to Document

10.1109/ECCE50734.2022.9948141

Cite this

Alden, R. E., Jones, E. S., Poore, S. B., Gong, H., Al Hadi, A., & Ionel, D. M. (2022). Digital Twin for HVAC Load and Energy Storage based on a Hybrid ML Model with CTA-2045 Controls Capability. In 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022 (2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022). https://doi.org/10.1109/ECCE50734.2022.9948141

@inproceedings{8bd8d5cd8e274009859c22348a99d34f,

title = "Digital Twin for HVAC Load and Energy Storage based on a Hybrid ML Model with CTA-2045 Controls Capability",

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. Widely used white box models, due to their complexity, are too computationally intensive to be employed in high resolution distributed energy resources (DER) platforms without simulation time delays. In this paper, an ultra-fast one-minute resolution Hybrid Machine Learning Model (HMLM) is proposed as part of a novel procedure to replicate white box models as an alternative to wide spread experimental big data collection. Synthetic output data from experimentally calibrated EnergyPlus models for three existing smart homes managed by the Tennessee Valley Authority is used. The HMLM employs combined k-means clustering and multiple linear regression (MLR) models to predict minutely HVAC power with satisfactory nRMSE error of less than 10\% across an entire year test set. An approach is provided to characterize HVAC systems through the newly proposed hybrid model as a generalized storage (GES) device suitable for DER control and event types in accordance with the Communication Technology Association (CTA) 2045 standard and Energy Star metrics such as “energy take”, currently developed by industry, to unify household appliance controls.",

keywords = "ANSI/CTA-2045-B, Battery Energy Storage System (BESS), Demand Response (DR), Energy Star, Energy Storage, Energy Take, Heating Ventilation and Air Conditioning (HVAC), Home Energy Management (HEM), machine learning, smart grid, smart homes",

author = "Alden, \{Rosemary E.\} and Jones, \{Evan S.\} and Poore, \{Steven B.\} and Huangjie Gong and \{Al Hadi\}, Abdullah and Ionel, \{Dan M.\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022 ; Conference date: 09-10-2022 Through 13-10-2022",

year = "2022",

doi = "10.1109/ECCE50734.2022.9948141",

language = "English",

series = "2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022",

booktitle = "2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022",

}

Alden, RE, Jones, ES, Poore, SB, Gong, H, Al Hadi, A & Ionel, DM 2022, Digital Twin for HVAC Load and Energy Storage based on a Hybrid ML Model with CTA-2045 Controls Capability. in 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022. 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022, 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022, Detroit, United States, 10/9/22. https://doi.org/10.1109/ECCE50734.2022.9948141

Digital Twin for HVAC Load and Energy Storage based on a Hybrid ML Model with CTA-2045 Controls Capability. / Alden, Rosemary E.; Jones, Evan S.; Poore, Steven B. et al.
2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022. 2022. (2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Digital Twin for HVAC Load and Energy Storage based on a Hybrid ML Model with CTA-2045 Controls Capability

AU - Alden, Rosemary E.

AU - Jones, Evan S.

AU - Poore, Steven B.

AU - Gong, Huangjie

AU - Al Hadi, Abdullah

AU - Ionel, Dan M.

PY - 2022

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N2 - 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. Widely used white box models, due to their complexity, are too computationally intensive to be employed in high resolution distributed energy resources (DER) platforms without simulation time delays. In this paper, an ultra-fast one-minute resolution Hybrid Machine Learning Model (HMLM) is proposed as part of a novel procedure to replicate white box models as an alternative to wide spread experimental big data collection. Synthetic output data from experimentally calibrated EnergyPlus models for three existing smart homes managed by the Tennessee Valley Authority is used. The HMLM employs combined k-means clustering and multiple linear regression (MLR) models to predict minutely HVAC power with satisfactory nRMSE error of less than 10% across an entire year test set. An approach is provided to characterize HVAC systems through the newly proposed hybrid model as a generalized storage (GES) device suitable for DER control and event types in accordance with the Communication Technology Association (CTA) 2045 standard and Energy Star metrics such as “energy take”, currently developed by industry, to unify household appliance controls.

AB - 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. Widely used white box models, due to their complexity, are too computationally intensive to be employed in high resolution distributed energy resources (DER) platforms without simulation time delays. In this paper, an ultra-fast one-minute resolution Hybrid Machine Learning Model (HMLM) is proposed as part of a novel procedure to replicate white box models as an alternative to wide spread experimental big data collection. Synthetic output data from experimentally calibrated EnergyPlus models for three existing smart homes managed by the Tennessee Valley Authority is used. The HMLM employs combined k-means clustering and multiple linear regression (MLR) models to predict minutely HVAC power with satisfactory nRMSE error of less than 10% across an entire year test set. An approach is provided to characterize HVAC systems through the newly proposed hybrid model as a generalized storage (GES) device suitable for DER control and event types in accordance with the Communication Technology Association (CTA) 2045 standard and Energy Star metrics such as “energy take”, currently developed by industry, to unify household appliance controls.

KW - ANSI/CTA-2045-B

KW - Battery Energy Storage System (BESS)

KW - Demand Response (DR)

KW - Energy Star

KW - Energy Storage

KW - Energy Take

KW - Heating Ventilation and Air Conditioning (HVAC)

KW - Home Energy Management (HEM)

KW - machine learning

KW - smart grid

KW - smart homes

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UR - https://www.scopus.com/pages/publications/85142106513#tab=citedBy

U2 - 10.1109/ECCE50734.2022.9948141

DO - 10.1109/ECCE50734.2022.9948141

M3 - Conference contribution

AN - SCOPUS:85142106513

T3 - 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022

BT - 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022

T2 - 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022

Y2 - 9 October 2022 through 13 October 2022

ER -

Digital Twin for HVAC Load and Energy Storage based on a Hybrid ML Model with CTA-2045 Controls Capability

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this