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
Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks
TL;DR: A recurrent neural network model to make medium-to-long term predictions of electricity consumption profiles in commercial and residential buildings at one-hour resolution and uses the deep NN to perform imputation on an electricity consumption dataset containing segments of missing values is presented.
Journal ArticleDOI
Modeling and forecasting building energy consumption: A review of data-driven techniques
TL;DR: A review of studies developing data-driven models for building scale applications with a focus on the input data characteristics and data pre-processing methods, the building typologies considered, the targeted energy end-uses and forecasting horizons, and accuracy assessment.
Journal ArticleDOI
Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms
TL;DR: A novel modeling framework for forecasting electricity prices is proposed and it is shown how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant.
Journal ArticleDOI
Roles of artificial intelligence in construction engineering and management: A critical review and future trends
Yue Pan,Limao Zhang +1 more
TL;DR: A systematic review under both scientometric and qualitative analysis is presented to present the current state of AI adoption in the context of CEM and discuss its future research trends.
Journal ArticleDOI
Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques
TL;DR: Among all of the investigated deep learning techniques, the gated 24-h CNN model, performed in a direct multi-step manner, proves itself to have the best performance, improving the forecasting accuracy by 22.6% compared to that of the seasonal ARIMAX.
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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