Journal ArticleDOI
Data-driven framework towards realistic bottom-up energy benchmarking using an Artificial Neural Network
TLDR
In this paper , a standard framework for data compiling is proposed and an assessment of the uncertainty of variables using entropy and cluster analysis allowed to obtain representative archetypes, and an Artificial Neural Network (ANN) was used as a predictive tool, and it was applied to benchmark a sample of actual buildings.About:
This article is published in Applied Energy.The article was published on 2022-01-01. It has received 15 citations till now. The article focuses on the topics: Benchmarking & Computer science.read more
Citations
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Journal ArticleDOI
Bridging the gap between complexity and interpretability of a data analytics-based process for benchmarking energy performance of buildings
TL;DR: In this paper , an explainable AI-based benchmarking framework for estimating the membership to specific energy performance classes of a large set of Energy Performance Certificates (EPCs) of flats is proposed.
Journal ArticleDOI
Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking
Abigail Andrews,Rishee K. Jain +1 more
TL;DR: In this article , the authors proposed a four-step method for embedding grid interactivity and demand flexibility into building benchmarking models that utilizes emerging building and time-series electricity data streams.
Journal ArticleDOI
Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends
TL;DR: In this paper , a review of building energy modeling techniques and state-of-the-art updates of model predictive control (MPC) in HVAC applications is presented.
Journal ArticleDOI
Novel Neural Network Optimized by Electrostatic Discharge Algorithm for Modification of Buildings Energy Performance
Arash Mohammadi Fallah,E. Ghafourian,Ladan Shahzamani Sichani,Hossein Ghafourian,Behdad Arandian,Moncef L. Nehdi +5 more
TL;DR: In this article , the authors proposed an integrative machine learning model for predicting two energy parameters of residential buildings, namely annual thermal energy demand (DThE) and annual weighted average discomfort degree-hours (HDD).
Journal ArticleDOI
Prediction of thermophysical properties of chlorine eutectic salts via artificial neural network combined with polar bear optimization
Yang Tian,Xianglei Liu,Li Zhang,Qinyang Luo,Qiao Xu,Haichen Yao,Feng-Qing Yang,Jianguo Wang,Chunzhuo Dang,Yiming Xuan +9 more
TL;DR: In this paper , a prediction model of thermophysical properties of eutectic salts based on the backpropagation (BP) artificial neural network method combined with bio-inspired algorithms (polar bear optimization (PBO) or genetic algorithm (GA)) is proposed.
References
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Journal ArticleDOI
Finding Groups in Data: An Introduction to Cluster Analysis.
Journal ArticleDOI
Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks
TL;DR: This study presents three modeling techniques for the prediction of electricity energy consumption: decision tree and neural networks are considered, and model selection is based on the square root of average squared error.
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Urban building energy modeling – A review of a nascent field
TL;DR: In this paper, a review of emerging simulation methods and implementation workflows for bottom-up urban building energy models (UBEM) is presented, as well as an outlook for future developments.
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Quantifying the influence of environmental and water conservation attitudes on household end use water consumption
Rachelle McDonald Willis,Rodney Anthony Stewart,Kriengsak Panuwatwanich,Philip Williams,Anna L. Hollingsworth +4 more
TL;DR: Results indicated that residents with very positive environmental and water conservation attitudes consumed significantly less water in total and across the behaviourally influenced end uses of shower, clothes washer, irrigation and tap, than those with moderately positive attitudinal concern.
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Review of building energy-use performance benchmarking methodologies
TL;DR: In this article, the authors review what kinds of mathematical methods used in developing benchmarking systems, discuss the properties of the methods, and classify two kinds of benchmarking system based on their properties.