Smart Buildings: Comparison of Various Deep Learning Models to Forecast Energy Consumption

C. H. Li; K. Y. Tam; T. T. Lee; S. L. Mak; S. K. Lam; C. C. Lee; T. W. Chan; W. F. Tang; C. Ng; H. Y. Yuen

doi:10.1007/978-981-99-8031-4_34

Smart Buildings: Comparison of Various Deep Learning Models to Forecast Energy Consumption

C. H. Li, K. Y. Tam, T. T. Lee, S. L. Mak, S. K. Lam, C. C. Lee, T. W. Chan, W. F. Tang, C. Ng, H. Y. Yuen

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

Abstract

Smart buildings are increasing around the world as they offer a range of benefits including energy efficiency, cost savings, and improved occupant comfort. Equipped with the different innovation technologies like artificial intelligence and machine learning. Such technologies are applied to predict energy consumption in smart buildings. Deep learning, a subset of machine learning, has become more popular in recent years due to its capability of learning complex patterns from large datasets. In this paper, the application of deep learning on energy consumption prediction of smart buildings is reviewed. An overview of smart buildings and energy consumption prediction are provided. The basic principles of deep learning and its application in smart buildings are then discussed. The benefits and drawbacks of several deep learning models developed for smart building energy consumption prediction are discussed. Finally, future research directions for applications of deep learning to forecast energy consumption in smart buildings are concluded.

Original language	English
Title of host publication	Intelligent Sustainable Systems - Selected Papers of WorldS4 2023
Editors	Atulya K. Nagar, Dharm Singh Jat, Durgesh Mishra, Amit Joshi
Pages	391-401
Number of pages	11
DOIs	https://doi.org/10.1007/978-981-99-8031-4_34
Publication status	Published - 2024
Event	World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2023 - London, United Kingdom Duration: 21 Aug 2023 → 24 Aug 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	812
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2023
Country/Territory	United Kingdom
City	London
Period	21/08/23 → 24/08/23

Keywords

Deep learning
Energy consumption
Neutral network
Smart building

UN SDGs

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

Access to Document

10.1007/978-981-99-8031-4_34

Cite this

Li, C. H., Tam, K. Y., Lee, T. T., Mak, S. L., Lam, S. K., Lee, C. C., Chan, T. W., Tang, W. F., Ng, C., & Yuen, H. Y. (2024). Smart Buildings: Comparison of Various Deep Learning Models to Forecast Energy Consumption. In A. K. Nagar, D. S. Jat, D. Mishra, & A. Joshi (Eds.), Intelligent Sustainable Systems - Selected Papers of WorldS4 2023 (pp. 391-401). (Lecture Notes in Networks and Systems; Vol. 812). https://doi.org/10.1007/978-981-99-8031-4_34

@inproceedings{60037b6ab99343c6b6cb1baa689d476f,

title = "Smart Buildings: Comparison of Various Deep Learning Models to Forecast Energy Consumption",

abstract = "Smart buildings are increasing around the world as they offer a range of benefits including energy efficiency, cost savings, and improved occupant comfort. Equipped with the different innovation technologies like artificial intelligence and machine learning. Such technologies are applied to predict energy consumption in smart buildings. Deep learning, a subset of machine learning, has become more popular in recent years due to its capability of learning complex patterns from large datasets. In this paper, the application of deep learning on energy consumption prediction of smart buildings is reviewed. An overview of smart buildings and energy consumption prediction are provided. The basic principles of deep learning and its application in smart buildings are then discussed. The benefits and drawbacks of several deep learning models developed for smart building energy consumption prediction are discussed. Finally, future research directions for applications of deep learning to forecast energy consumption in smart buildings are concluded.",

keywords = "Deep learning, Energy consumption, Neutral network, Smart building",

author = "Li, {C. H.} and Tam, {K. Y.} and Lee, {T. T.} and Mak, {S. L.} and Lam, {S. K.} and Lee, {C. C.} and Chan, {T. W.} and Tang, {W. F.} and C. Ng and Yuen, {H. Y.}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2023 ; Conference date: 21-08-2023 Through 24-08-2023",

year = "2024",

doi = "10.1007/978-981-99-8031-4_34",

language = "English",

isbn = "9789819980307",

series = "Lecture Notes in Networks and Systems",

pages = "391--401",

editor = "Nagar, {Atulya K.} and Jat, {Dharm Singh} and Durgesh Mishra and Amit Joshi",

booktitle = "Intelligent Sustainable Systems - Selected Papers of WorldS4 2023",

}

Li, CH, Tam, KY, Lee, TT, Mak, SL, Lam, SK , Lee, CC, Chan, TW, Tang, WF , Ng, C & Yuen, HY 2024, Smart Buildings: Comparison of Various Deep Learning Models to Forecast Energy Consumption. in AK Nagar, DS Jat, D Mishra & A Joshi (eds), Intelligent Sustainable Systems - Selected Papers of WorldS4 2023. Lecture Notes in Networks and Systems, vol. 812, pp. 391-401, World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2023, London, United Kingdom, 21/08/23. https://doi.org/10.1007/978-981-99-8031-4_34

Smart Buildings: Comparison of Various Deep Learning Models to Forecast Energy Consumption. / Li, C. H.; Tam, K. Y.; Lee, T. T. et al.
Intelligent Sustainable Systems - Selected Papers of WorldS4 2023. ed. / Atulya K. Nagar; Dharm Singh Jat; Durgesh Mishra; Amit Joshi. 2024. p. 391-401 (Lecture Notes in Networks and Systems; Vol. 812).

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

TY - GEN

T1 - Smart Buildings

T2 - World Conference on Smart Trends in Systems, Security and Sustainability, WS4 2023

AU - Li, C. H.

AU - Tam, K. Y.

AU - Lee, T. T.

AU - Mak, S. L.

AU - Lam, S. K.

AU - Lee, C. C.

AU - Chan, T. W.

AU - Tang, W. F.

AU - Ng, C.

AU - Yuen, H. Y.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - Smart buildings are increasing around the world as they offer a range of benefits including energy efficiency, cost savings, and improved occupant comfort. Equipped with the different innovation technologies like artificial intelligence and machine learning. Such technologies are applied to predict energy consumption in smart buildings. Deep learning, a subset of machine learning, has become more popular in recent years due to its capability of learning complex patterns from large datasets. In this paper, the application of deep learning on energy consumption prediction of smart buildings is reviewed. An overview of smart buildings and energy consumption prediction are provided. The basic principles of deep learning and its application in smart buildings are then discussed. The benefits and drawbacks of several deep learning models developed for smart building energy consumption prediction are discussed. Finally, future research directions for applications of deep learning to forecast energy consumption in smart buildings are concluded.

AB - Smart buildings are increasing around the world as they offer a range of benefits including energy efficiency, cost savings, and improved occupant comfort. Equipped with the different innovation technologies like artificial intelligence and machine learning. Such technologies are applied to predict energy consumption in smart buildings. Deep learning, a subset of machine learning, has become more popular in recent years due to its capability of learning complex patterns from large datasets. In this paper, the application of deep learning on energy consumption prediction of smart buildings is reviewed. An overview of smart buildings and energy consumption prediction are provided. The basic principles of deep learning and its application in smart buildings are then discussed. The benefits and drawbacks of several deep learning models developed for smart building energy consumption prediction are discussed. Finally, future research directions for applications of deep learning to forecast energy consumption in smart buildings are concluded.

KW - Deep learning

KW - Energy consumption

KW - Neutral network

KW - Smart building

UR - http://www.scopus.com/inward/record.url?scp=85187779737&partnerID=8YFLogxK

U2 - 10.1007/978-981-99-8031-4_34

DO - 10.1007/978-981-99-8031-4_34

M3 - Conference contribution

AN - SCOPUS:85187779737

SN - 9789819980307

T3 - Lecture Notes in Networks and Systems

SP - 391

EP - 401

BT - Intelligent Sustainable Systems - Selected Papers of WorldS4 2023

A2 - Nagar, Atulya K.

A2 - Jat, Dharm Singh

A2 - Mishra, Durgesh

A2 - Joshi, Amit

Y2 - 21 August 2023 through 24 August 2023

ER -

Smart Buildings: Comparison of Various Deep Learning Models to Forecast Energy Consumption

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Cite this