Showing papers in "Energy and Buildings in 2018"
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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.
331 citations
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TL;DR: In this paper, a review of phase change materials used to optimize building envelope and equipment is provided, and the existing gaps in the research works on energy performance improvement with phase change material are identified, and recommendations offered as authors' viewpoints in 5 aspects.
275 citations
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TL;DR: The attempt to rethink occupant behavior and its role in building energy performance by means of review identifies four existing research gaps, namely the needs for understanding occupant behavior in a systematic framework, for stronger empirical evidence beyond individual buildings and at a larger city scale, and for linking occupant behavior to socio-economic and policy variables.
264 citations
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TL;DR: In this article, a composite phase change materials (c-PCMs) composed of polyethylene glycol (PEG) and biological porous carbon (BPC) is investigated.
253 citations
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TL;DR: The results show that using the gradient boosting machine model improved the R‐squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.
218 citations
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TL;DR: A reinforcement learning control strategy is introduced that makes optimal control decisions for HVAC and window systems to minimize both energy consumption and thermal discomfort and is able to adapt to stochastic occupancy and occupant behaviors.
194 citations
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TL;DR: The measures for improving the flexibility of commercial and residential buildings are summarized, a systematic methodology framework to evaluate energy demand flexibility in buildings is developed, and a synergistic approach with various measures is advisable.
190 citations
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TL;DR: In this paper, a comprehensive review of energy forecasting models that are specified in the literature part is also provided, highlighting strengths, shortcomings, and purpose of the methods of numerous data-mining based approaches.
169 citations
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TL;DR: A comprehensive review on building occupancy estimation and detection is presented and some potential future research directions are indicated based on current progresses of the systems.
162 citations
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TL;DR: In this article, a shape-stabilized composite phase change materials (S-SCPCMs) were used to generate various building sections with solar energy harvesting/releasing capability.
158 citations
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TL;DR: A comprehensive review on the current utilization of unsupervised data analytics in mining massive building operational data is provided, according to their knowledge representations and applications.
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TL;DR: In this article, a two-part survey was conducted to identify the needs, benefits, and hindrances of applying Building Energy Simulation and Optimization (BESO) to sustainable building design.
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TL;DR: A weighted pixelated image of the voltage–current trajectory is used as input data for a deep learning method: a convolutional neural network that will automatically extract key features for appliance classification.
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TL;DR: In this paper, a literature review on smart ventilation used in residential buildings, based on energy and indoor air quality performance, is presented, which includes 38 studies of various smart ventilation systems with control based on CO2, humidity, combined CO2 and total volatile organic compounds (TVOC), occupancy, or outdoor temperature.
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TL;DR: In this paper, a review of the literature involving synergy of PCM and mortar for achieving energy efficiency in buildings is presented, which includes details regarding different PCM-mortar combinations along with details pertaining to, but not limited to, thermal and mechanical properties.
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TL;DR: A review and applied evaluation of existing definitions and quantification methodologies for energy flexibility shows that energy flexibility definitions found in the literature have their particularities despite sharing the same principle thatEnergy flexibility is the ability to adapt the energy profile without jeopardizing technical and comfort constraints.
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TL;DR: Hephaestus is proposed, a novel transfer learning method for cross-building energy forecasting based on time series multi-feature regression with seasonal and trend adjustment that improves energy prediction accuracy for a new building with limited data by using datasets from other similar buildings.
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TL;DR: A Net Zero Energy Building (NZEB) is a building with characteristics such as equal energy generation to usage, significantly reduced energy demands, energy costs equalling zero or net zero greenhouse gas (GHG) emissions as mentioned in this paper.
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TL;DR: A NILM algorithm based on the Deep Neural Networks is proposed, which outperforms the AFAMAP algorithm both in seen and unseen condition, and that it exhibits a significant robustness in presence of noise.
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TL;DR: A thorough survey and review of building energy performance gap research was carried out and an in-depth analysis of BEPG research was conducted to explore its causes and corresponding strategies (including design concept, “hard” technologies and “soft” measures).
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TL;DR: In this article, a non-intrusive infrared thermography framework is proposed to estimate an occupant's thermal comfort level by measuring skin temperature collected from different facial regions using low-cost thermal cameras.
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TL;DR: A computational modeling approach to accurately predict the mean wind pressure coefficient on the surfaces of flat-, gable- and hip-roofed rectangular buildings using artificial neural network to estimate the surface-average pressure coefficient according to the building geometry and the wind angle is presented.
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TL;DR: In this paper, the authors investigated the possibility to predict human thermal state (Comfort/Discomfort) from the information of physiological parameters, which can facilitate informed control decision of ambient thermal-conditioning in a building environment.
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TL;DR: The analysis demonstrates that, for the techniques applied, the variables reported in CBECS have inadequate predictive power to map actual energy consumption, and suggests filling information gaps in areas such as occupant behavior, power management, building thermal performance, and their interactions may help to improve predictive modeling.
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TL;DR: This paper proposes WiFree, a novel device-free occupancy detection and crowd counting scheme using only commercial WiFi enabled the Internet of Things (IoT) devices and designs a novel IoT platform with which the fine-grand channel state information (CSI) measurements can be obtained directly from pervasive IoT devices.
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TL;DR: In this article, the authors proposed a novel occupancy-driven lighting control system that aims to reduce energy consumption while simultaneously preserving the lighting comfort of occupants, by leveraging the fine-grained occupancy information estimated by existing WiFi infrastructure in a nonintrusive manner.
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TL;DR: In this paper, a natural fiber material in the form of wood waste is examined experimentally to assess its suitability for use as a thermal insulation material, without the addition of any binder, within a timber frame wall construction.
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TL;DR: A novel activity-based multi-agent approach for urban occupant behavior modeling is proposed as alternative to current approaches and seems to be superior to deterministic models.
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TL;DR: In this article, an ensemble bagging tree (EBT) was used to predict hourly electricity demand of the test building with improved accuracy of Mean Absolute Prediction Error that ranged from 2.97% to 4.63%.
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TL;DR: In this article, a method termed as Predicted Thermal State (PTS) model is presented, which uses the peripheral skin temperature and its gradient features from a single body location to evaluate the thermal state.