A short-term building cooling load prediction method using deep learning algorithms

•Deep learning-based methods are developed for building cooling load prediction.•Accurate predictions on 24-h ahead building cooling load profiles are achieved.•The potentials of supervised and unsupervised deep learning is investigated.•Features extracted by unsupervised deep learning can improve m...

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Vydáno v:	Applied energy Ročník 195; s. 222 - 233
Hlavní autoři:	Fan, Cheng, Xiao, Fu, Zhao, Yang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.06.2017
Témata:	algorithms artificial intelligence Big data Building cooling load Building energy prediction buildings cooling data analysis Data mining Deep learning energy prediction Deep learning Building energy prediction Big data Data mining Building cooling load
ISSN:	0306-2619, 1872-9118
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	•Deep learning-based methods are developed for building cooling load prediction.•Accurate predictions on 24-h ahead building cooling load profiles are achieved.•The potentials of supervised and unsupervised deep learning is investigated.•Features extracted by unsupervised deep learning can improve model performance.•Supervised deep learning does not show obvious advantages in model development. Short-term building cooling load prediction is the essential foundation for many building energy management tasks, such as fault detection and diagnosis, demand-side management and control optimization. Conventional methods, which heavily rely on physical principles, have limited power in practice as their performance is subject to many physical assumptions. By contrast, data-driven methods have gained huge interests due to their flexibility in model development and the rich data available in modern buildings. The rapid development in data science has provided advanced data analytics to tackle prediction problems in a more convenient, efficient and effective way. This paper investigates the potential of one of the most promising techniques in advanced data analytics, i.e., deep learning, in predicting 24-h ahead building cooling load profiles. Deep learning refers to a collection of machine learning algorithms which are powerful in revealing nonlinear and complex patterns in big data. Deep learning can be used either in a supervised manner to develop prediction models with given inputs and output (i.e., cooling load), or in an unsupervised manner to extract meaningful features from raw data as model inputs. This study exploits the potential of deep learning in both manners, and compares its performance in cooling load prediction with typical feature extraction methods and popular prediction techniques in the building field. The results show that deep learning can enhance the performance of building cooling load prediction, especially when used in an unsupervised manner for constructing high-level features as model inputs. Using the features extracted by unsupervised deep learning as inputs for cooling load prediction can evidently enhance the prediction performance. The findings are enlightening and could bring more flexible and effective solutions for building energy predictions.
AbstractList	Short-term building cooling load prediction is the essentialfoundation for manybuilding energy managementtasks, such as fault detection and diagnosis, demand-side management and control optimization.Conventional methods, which heavily rely on physical principles,have limited power in practiceas their performance is subjectto many physical assumptions. By contrast, data-driven methods have gained hugeinterests due to their flexibility in model development and the rich dataavailable in modern buildings. The rapid development in data sciencehas provided advanced data analytics to tackle prediction problems in a more convenient, efficient and effective way.This paper investigates the potential of one of the most promisingtechniquesin advanced data analytics, i.e., deep learning, in predicting 24-hour ahead building cooling load profiles.Deep learning refers to a collection of machine learning algorithms whichare powerful in revealing nonlinear and complex patterns inbig data. Deep learning can be used either in a supervised manner todevelop prediction models with given inputs and output (i.e., cooling load), or in an unsupervised manner to extract meaningfulfeatures from raw data as model inputs. This study exploits the potential of deep learning in both manners, and compares its performance incooling load predictionwith typical feature extraction methods and popular prediction techniques in the building field. The results show that deep learning can enhance the performance of building cooling load prediction, especially when used in an unsupervised manner for constructing high-level features as model inputs.Using the features extracted by unsupervised deep learning as inputs for cooling load prediction can evidently enhance the prediction performance. The findings are enlightening and could bring more flexible and effective solutions for building energy predictions. Short-term building cooling load prediction is the essential foundation for many building energy management tasks, such as fault detection and diagnosis, demand-side management and control optimization. Conventional methods, which heavily rely on physical principles, have limited power in practice as their performance is subject to many physical assumptions. By contrast, data-driven methods have gained huge interests due to their flexibility in model development and the rich data available in modern buildings. The rapid development in data science has provided advanced data analytics to tackle prediction problems in a more convenient, efficient and effective way.This paper investigates the potential of one of the most promising techniques in advanced data analytics, i.e., deep learning, in predicting 24-h ahead building cooling load profiles. Deep learning refers to a collection of machine learning algorithms which are powerful in revealing nonlinear and complex patterns in big data. Deep learning can be used either in a supervised manner to develop prediction models with given inputs and output (i.e., cooling load), or in an unsupervised manner to extract meaningful features from raw data as model inputs. This study exploits the potential of deep learning in both manners, and compares its performance in cooling load prediction with typical feature extraction methods and popular prediction techniques in the building field. The results show that deep learning can enhance the performance of building cooling load prediction, especially when used in an unsupervised manner for constructing high-level features as model inputs. Using the features extracted by unsupervised deep learning as inputs for cooling load prediction can evidently enhance the prediction performance. The findings are enlightening and could bring more flexible and effective solutions for building energy predictions. •Deep learning-based methods are developed for building cooling load prediction.•Accurate predictions on 24-h ahead building cooling load profiles are achieved.•The potentials of supervised and unsupervised deep learning is investigated.•Features extracted by unsupervised deep learning can improve model performance.•Supervised deep learning does not show obvious advantages in model development. Short-term building cooling load prediction is the essential foundation for many building energy management tasks, such as fault detection and diagnosis, demand-side management and control optimization. Conventional methods, which heavily rely on physical principles, have limited power in practice as their performance is subject to many physical assumptions. By contrast, data-driven methods have gained huge interests due to their flexibility in model development and the rich data available in modern buildings. The rapid development in data science has provided advanced data analytics to tackle prediction problems in a more convenient, efficient and effective way. This paper investigates the potential of one of the most promising techniques in advanced data analytics, i.e., deep learning, in predicting 24-h ahead building cooling load profiles. Deep learning refers to a collection of machine learning algorithms which are powerful in revealing nonlinear and complex patterns in big data. Deep learning can be used either in a supervised manner to develop prediction models with given inputs and output (i.e., cooling load), or in an unsupervised manner to extract meaningful features from raw data as model inputs. This study exploits the potential of deep learning in both manners, and compares its performance in cooling load prediction with typical feature extraction methods and popular prediction techniques in the building field. The results show that deep learning can enhance the performance of building cooling load prediction, especially when used in an unsupervised manner for constructing high-level features as model inputs. Using the features extracted by unsupervised deep learning as inputs for cooling load prediction can evidently enhance the prediction performance. The findings are enlightening and could bring more flexible and effective solutions for building energy predictions.
Author	Fan, Cheng Xiao, Fu Zhao, Yang
Author_xml	– sequence: 1 givenname: Cheng surname: Fan fullname: Fan, Cheng organization: Department of Construction Management and Real Estate, Shenzhen University, Shenzhen, China – sequence: 2 givenname: Fu orcidid: 0000-0002-3779-3943 surname: Xiao fullname: Xiao, Fu email: linda.xiao@polyu.edu.hk organization: Department of Building Services Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China – sequence: 3 givenname: Yang surname: Zhao fullname: Zhao, Yang organization: Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou, China
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Snippet	•Deep learning-based methods are developed for building cooling load prediction.•Accurate predictions on 24-h ahead building cooling load profiles are... Short-term building cooling load prediction is the essentialfoundation for manybuilding energy managementtasks, such as fault detection and diagnosis,... Short-term building cooling load prediction is the essential foundation for many building energy management tasks, such as fault detection and diagnosis,...
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SubjectTerms	algorithms artificial intelligence Big data Building cooling load Building energy prediction buildings cooling data analysis Data mining Deep learning energy prediction
Title	A short-term building cooling load prediction method using deep learning algorithms
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