Showing papers in "Energy and Buildings in 2021"

TL;DR: In this article, a detailed investigation of various types of nanoparticles used to enhance the thermo-physical characteristics of shape stabilized composite phase change material (ss-CPCM) was conducted.

TL;DR: This survey does not analyze existing products or realized buildings, but provides an overview of the technologies for BIPV, and presents the wide range of technical design options, ranging from sub-module level design parameters to building energy systems.

TL;DR: In this paper, a comprehensive urbanization framework from three dimensions was established by combining the extended Kaya identity and Monte Carlo simulation approach to explore future dynamic evolution trajectory, possible peaks and peaking time of China's building carbon emissions from 2000 to 2050, considering the uncertainties of factors.

TL;DR: Wang et al. as discussed by the authors proposed a smart low-cost ventilation control strategy based on occupant density-detection algorithm with consideration of both infection prevention and energy efficiency, which can automatically adjust between the demand-controlled mode and anti-infection mode with a self-developed low cost hardware prototype.

TL;DR: This study presents a prediction strategy of building energy consumption based on ensemble learning and energy consumption patterns classification and illustrates that the proposed strategy is reliable and effective and can obtain acceptable performance with less training data, which is helpful to the application of energy consumption prediction.

TL;DR: The rough set theory was used to reduce the redundant influencing factors ofBuilding energy consumption and find the critical factors of building energy consumption to form a deep neural network with a “deep” architecture and powerful capabilities in extracting features.

TL;DR: A generalized framework based on existing literature for different urban energy modeling methods is proposed to assist urban planners and energy policymakers when choosing appropriate methods to develop and implement in-depth sustainable building energy planning and analysis projects based on limited available resources.

TL;DR: A new efficient multi-objective optimization method, based on the Building Performance Optimization (BPO) technique, has been developed to improve the indoor thermal comfort and energy performance of residential buildings, i.e. a Moroccan ground floor + first floor (GFFF) house located in Marrakech region.

TL;DR: Numerical analyses and field studies show the potential of the technology to achieve relevant energy savings with respect to conventional glazing systems, especially in cold dominated climates and the fully developed technology may become competitive with the performance required in a zero energy building perspective.

TL;DR: In this article, an up-to-date review on phase change materials (PCMs) in the context of latent TES in the building sector is presented, summarizing its performance, applications, and key challenges.

TL;DR: This study aims to develop a co-simulation framework between BES tool (EnergyPlus) and CFD tool (Ansys Fluent) to model the PCM integrated built environment and compare its prediction accuracy with the most popular B ES tool, i.e., EnergyPlus.

TL;DR: In this paper, the authors present a complete in-depth review of aerogel incorporated cementitious materials in terms of production/synthetisation, fresh rheology and proportioning, mechanical, microstructural, and durability properties, including water absorption, capillary water absorption and fire resistance.

TL;DR: A systematic analysis evidences the lack of standardization and disagreement regarding the assessment of coefficients, database source, and boundary system used in the methodology assessment in embodied energy and embodied carbon assessments.

TL;DR: In this article, the authors investigated the possibility to contain COVID-19 contagion in indoor environments via increasing ventilation rates obtained through high energy efficiency systems combining thermal recovery by heat exchanger and thermodynamic recovery of heat pump.

TL;DR: The outcomes of this study help researchers and designers to apply DOEs that consider the extent of nonlinearity and interaction of factors in the investigated process in order to select the most successful and the most efficient designs for the specific process characterization.

TL;DR: Results show that machine learning models can achieve accurate building heating and cooling EUI prediction, with the polynomial kernel support vector regression showing the best accuracy at the level of a single building, and the Gaussian radial basis function kernel support vectors regression performing the best at the stock level.

TL;DR: A general data mining-based framework that can extract typical electricity load patterns (TELPs) and discover insightful information hidden in the patterns and improve the interpretability of clustering results to explain the relations between dynamic influencing factors related to electricity consumption and TELPs is proposed.

TL;DR: A definition and a set of criteria —vulnerability, resistance, robustness, and recoverability— that can help to develop intrinsic performance-driven indicators and functions of passive and active cooling solutions in buildings against two disruptors of indoor thermal environmental quality—heat waves and power outages are suggested.

TL;DR: A meta-analysis and categorization of the simulation inputs and outputs, data type and resolution, key calibration methods, and calibration performance evaluation is conducted and an incremental approach is proposed to encourage future research’s reproducibility.

TL;DR: The proposed investigation is aimed at providing useful suggestions and guidelines for the renovation of educational buildings, in order to do University classrooms safe and sustainable indoor places, with respect to the 2020 SARS-CoV-2 global pandemic.

TL;DR: A transfer-learning-based methodology for fault diagnosis in building chillers and the experimental results validate the value of transfer learning for FDD in building energy systems, especially when the experimental data available for model development are limited.

TL;DR: Deep Q-Network is developed with an action processor, defining the environment as a Partially Observable Markov Decision Process (POMDP) with a reward function consisting of energy cost (time-of-use and peak demand charges) and a discomfort penalty, which is an extension of most reward functions used in existing DRL works in this area.

TL;DR: A critical review on the state-of-the-art of cooling strategies, with special attention to their performance under heatwaves and power outages, proposed a definition of resilient cooling and described four criteria for resilience and used them to qualitatively evaluate the resilience of each strategy.

TL;DR: In this article, a new simulation-based optimisation method is developed, which uses climate models and Ant Colony Optimization to compare the energy-optimised designs under present and future climates.

TL;DR: The proposed physics-constrained control-oriented deep learning method incorporates structural priors from traditional physics-based building modeling into the neural network thermal dynamics model structure, thereby bounding predictions within physically realistic and safe operating ranges.

TL;DR: In this paper, seven deep recurrent neural networks (DRNNs) configurations are introudecd and tuned that can automatically detect faults of different degrees under the faulty and normal conditions, and a comprehensive study of hyperparameters is conducted to optimize and compare all the proposed configurations based on their accuracy and training computational time.

TL;DR: A systematic and comprehensive literature review is performed to identify the specific causes of the BEPG, and a life-cycle BIM engaged framework was developed that will assist researchers and practitioners better understand application of BIM to systematically improve building energy performance.

TL;DR: A multi-class NILM system which can detect in real-time any number of appliances and can be efficiently embedded into simple microprocessors and capable to automatically identify new appliances is proposed.

TL;DR: This study provides a methodology to select the suitable device types and determine their capacity size for IES under load uncertainty, which will benefit the design of a new IES.

TL;DR: The proposed CNN–LSTM algorithm can describe the complex change trend of the heating load and lead to a more accurate dynamic model of heating load with high nonlinearity and considerable thermal inertia delay, which can satisfy the field engineering applications' requirements in a more enhanced manner.