Journal ArticleDOI
Modeling and forecasting building energy consumption: A review of data-driven techniques
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TLDR
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.About:
This article is published in Sustainable Cities and Society.The article was published on 2019-07-01. It has received 422 citations till now. The article focuses on the topics: Energy consumption & Efficient energy use.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
구글 TensorFlow 소개
TL;DR: TensorFlow 2.0 in ActionTensor Flow 1.x Deep Learning Cookbook machine Learning with TensorFlow, Second EditionTensor flow 2 Pocket PrimerProgramming with Tensing, Tensor Flow Machine Learning Projects, and Hands-On Neural Networks.
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Machine learning prediction of mechanical properties of concrete: Critical review
TL;DR: Examination of several Machine Learning models for forecasting the mechanical properties of concrete, including artificial neural networks, support vector machine, decision trees, and evolutionary algorithms are examined.
Journal ArticleDOI
A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis
Deyslen Mariano-Hernández,Deyslen Mariano-Hernández,Luis Hernández-Callejo,Angel L. Zorita-Lamadrid,Oscar Duque-Perez,F. Santos García +5 more
TL;DR: A review of management strategies for building energy management systems for improving energy efficiency is presented and different management strategies are investigated in non-residential and residential buildings.
Journal ArticleDOI
Thermophysical properties and applications of nano-enhanced PCMs: An update review
TL;DR: In this article, the effects of nanoparticles on the most important thermophysical properties of phase change materials (PCMs) are discussed and the applications of nano-PCMs in the fields such as thermal energy storage (TES), thermal control unit (TCU), photovoltaic thermal thermal (PVT), solar still (SS), and building are examined.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Maximum likelihood from incomplete data via the EM algorithm
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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.
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LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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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.
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