Journal ArticleDOI
A review of machine learning in building load prediction
TLDR
This paper reviews the application of machine learning techniques in building load prediction under the organization and logic of the machine learning, which is to perform tasks T using Performance measure P and based on learning from Experience E.About:
This article is published in Applied Energy.The article was published on 2021-03-01. It has received 197 citations till now. The article focuses on the topics: Feature engineering & Analytics.read more
Citations
More filters
Journal ArticleDOI
Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives
TL;DR: In this paper , a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics, is presented, highlighting the role of deep learning in transfer learning in smart building applications.
Journal ArticleDOI
Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm
TL;DR: In this paper , a study on data-driven probabilistic machine learning (ML) techniques and their real-time applications to smart energy systems and networks was conducted to highlight the urgency of this area of research.
Journal ArticleDOI
Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles
TL;DR: This paper first reviews the application status of machine learning-based building energy efficiency research by analyzing the model implementation process and summarizing the main uses of the technology in the overall building energy management life cycle, and elaborates on the causes of, influences on, and potential solutions for practical issues found in the implementation and promotion of machineLearning-basedBuilding energy efficiency measures.
State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability
TL;DR: The state of the art of DL and ML methods used in this realm is presented through a novel taxonomy and it is disclosed that energy, health, and urban transport are the main domains of smart cities that DL andML methods contributed in to address their problems.
Journal ArticleDOI
Operational carbon transition in the megalopolises’ commercial buildings
TL;DR: In this paper , the decarbonization level of commercial buildings from China's five major megalopolises (Jing-Jin-Ji, Yangtze River Delta, Pearl River delta, YangTze River Middle Reach, and Cheng-Yu) through the generalized divisia index method, considering the impacts of socio-economy, technology evolution, and climate.
References
More filters
Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Journal ArticleDOI
Machine learning: Trends, perspectives, and prospects
TL;DR: The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
Journal ArticleDOI
Popular ensemble methods: an empirical study
David W. Opitz,Richard Maclin +1 more
TL;DR: This work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.
Journal ArticleDOI
A review on the prediction of building energy consumption
Hai‐Xiang Zhao,Frédéric Magoulès +1 more
TL;DR: In this paper, the authors present a review of recent developed models for predicting building energy consumption, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods, and further prospects are proposed for additional research reference.
Journal ArticleDOI
A review of data-driven building energy consumption prediction studies
Kadir Amasyali,Nora El-Gohary +1 more
TL;DR: A review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation is provided in this paper.
Related Papers (5)
A review of data-driven building energy consumption prediction studies
Kadir Amasyali,Nora El-Gohary +1 more