Journal ArticleDOI
Data driven prediction models of energy use of appliances in a low-energy house
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Data filtering to remove non-predictive parameters and feature ranking is discussed to include in energy prediction models and for building performance modeling.About:
This article is published in Energy and Buildings.The article was published on 2017-04-01. It has received 370 citations till now. The article focuses on the topics: Cross-validation & Gradient boosting.read more
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Random Forest based hourly building energy prediction
TL;DR: In this article, the authors proposed a homogeneous ensemble approach, i.e., use of Random Forest (RF), for hourly building energy prediction, which was adopted to predict the hourly electricity usage of two educational buildings in North Central Florida.
Journal ArticleDOI
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond
Sawsan Abdulrahman,Hanine Tout,Hakima Ould-Slimane,Azzam Mourad,Chamseddine Talhi,Mohsen Guizani +5 more
TL;DR: In this article, a survey of federated learning (FL) topics and research fields is presented, including core system models and designs, application areas, privacy and security, and resource management.
Journal ArticleDOI
Data-driven model predictive control using random forests for building energy optimization and climate control
Francesco Smarra,Francesco Smarra,Achin Jain,Tullio de Rubeis,Dario Ambrosini,Alessandro D'Innocenzo,Rahul Mangharam +6 more
TL;DR: Data-driven model predictive control (DPC) as discussed by the authors leverages machine learning algorithms such as regression trees and random forests to identify a predictive model of a building and derive a controller based only on the data.
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A review of the-state-of-the-art in data-driven approaches for building energy prediction
TL;DR: This paper provides a comprehensive review on building energy prediction, covering the entire data-driven process that includes feature engineering, potential data- driven models and expected outputs, and concludes with some potential future research directions based on discussion of existing research gaps.
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Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
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ggplot2: Elegant Graphics for Data Analysis
TL;DR: This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics.
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An introduction to statistical learning
TL;DR: An introduction to statistical learning provides an accessible overview of the essential toolset for making sense of the vast and complex data sets that have emerged in science, industry, and other sectors in the past twenty years.
Journal ArticleDOI
A review on buildings energy consumption information
TL;DR: In this article, the authors analyzed available information concerning energy consumption in buildings, and particularly related to HVAC systems, and compared different types of building types and end uses in different countries.
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Applied Predictive Modeling
Max Kuhn,Kjell Johnson +1 more
TL;DR: This research presents a novel and scalable approach called “Smartfitting” that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of designing and implementing statistical models for regression models.