Journal ArticleDOI
A short-term building cooling load prediction method using deep learning algorithms
Cheng Fan,Fu Xiao,Yang Zhao +2 more
TLDR
Wang et al. as mentioned in this paper investigated 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.About:
This article is published in Applied Energy.The article was published on 2017-06-01. It has received 462 citations till now. The article focuses on the topics: Cooling load & Deep learning.read more
Citations
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Journal ArticleDOI
Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization.
Man Li,Lingyun Dai,Yongjie Hu +2 more
TL;DR: A broad overview of the applications and future opportunities of ML methods on crucial topics in thermal energy research, from bottom-up materials discovery to top-down system design across atomistic levels to multi-scales, can be found in this article .
Journal ArticleDOI
Validation of virtual sensor-assisted Bayesian inference-based in-situ sensor calibration strategy for building HVAC systems
Proceedings ArticleDOI
Empirical Study of Massive Set-Point Behavioral Data: Towards a Cloud-Based Artificial Intelligence that Democratizes Thermostats
TL;DR: It was seen that people interacting more with the controllers tend to waste less energy, and it appears that adaptive thermal comfort theories that suggest users want lower temperatures in cold months are not reflected on the set-points chosen.
Proceedings ArticleDOI
Study on Machine Learning based Energy Efficiency in Developed Countries
Aditya Verma,Yogesh Kumar +1 more
TL;DR: A comprehensive review of the various researchers' contribution to forecasting the energy demand for various resources and the multiple machine learning techniques adopted to study the real data using time series models is presented.
Journal ArticleDOI
The influencing factors on efficacy enhancement of HVAC systems – A review
TL;DR: In this paper, various energy saving strategies for HVAC systems are investigated and different measures on further energy and cost savings in buildings are discussed, which can provide up to 62.1% electrical energy consumption reduction compared to buildings that are not designed for efficient energy usage.
References
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Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
Journal ArticleDOI
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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A review of data-driven building energy consumption prediction studies
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