Showing papers in "Energy and Buildings in 2020"

TL;DR: In this article, the authors reviewed and reported the recent progress and knowledge on the specific impact of current and projected urban overheating in energy, peak electricity demand, air quality, mortality and morbidity and urban vulnerability.

TL;DR: In this paper, a review of EU energy policies spanning over the last half century with a focus on policy instruments to encourage measures on energy efficiency in new and existing buildings is presented.

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.

TL;DR: This study systematically surveyed how machine learning has been applied at different stages of building life cycle and can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings.

TL;DR: The outcome of this review shows that data-driven based approaches are more promising for the FDD process of large-scale HVAC systems than model-based and knowledge-based ones.

TL;DR: The results demonstrate that the proposed DDPG and RDPG models have obvious advantages in forecasting building energy consumption compared to common supervised models, while accounting for more computation time for model training, and can be positively extended to other prediction problems, e.g., wind speed prediction and electricity load prediction.

TL;DR: This work presents a review of the current AI-based methodologies being used to enhance thermal comfort in indoor spaces using diverse machine learning (ML) algorithms and their deployment in building control systems for energy saving purposes.

TL;DR: Evidence is presented that adaptive comfort processes are relevant to the occupants of all buildings, including those that are air conditioned, as the thermal environmental exposures driving adaptation occur indoors where the authors spend most of their time.

TL;DR: In this paper, a systematic review of the technological options and strategies to achieve zero energy buildings was carried out to establish today state-of-the-art knowledge base and to present key design and performance factors that define those technologies with the final aim of contributing to climate change mitigation options of buildings.

TL;DR: A comprehensive literature review of the latest research on liquid desiccant air dehumidification, with an emphasis on manipulation methods from components, systems to materials, is provided in this paper.

TL;DR: This study proves that if the set of input variables are not carefully selected a dynamic deployment is strictly required for obtaining good performance with both static and dynamic deployment of the DRL controller.

TL;DR: Two deep learning models are proposed, which are a sequence-to-sequence (seq2seq) model and a two dimensional (2D) convolutional neural network (CNN) with an attention layer, and transfer learning framework to improve prediction accuracy for a target building.

TL;DR: This review of the topic spanning the last 21 years examines progress or lack thereof, in various research themes, including adaptive comfort theory, adaptive comfort practice (standards), contextual effects on adaptive comfort (building typologies), shifting boundaries of the comfort zone and the dynamics of comfort expectations.

TL;DR: This study applied nine ML algorithms and three data sampling methods to predict the 3-point and 7-point thermal sensation vote (TSV) in ASHRAE Comfort Database II and found Random Forest has the best performance among the tested algorithms.

TL;DR: In this review, new sensing technologies were summarized, their cons and pros were discussed, and extended applications for the demand-oriented ventilation were also reviewed as potential development and applications.

TL;DR: An extensive analysis was performed to evaluate the role of the SA in the building performance analysis (BPA), including the typical selection of the input and output parameters for the SA and various tools used in the SA were introduced to facilitate the practitioners.

TL;DR: Evaluating the prediction accuracy of the most popular BPS tools, namely TRNSYS, EnergyPlus and IDA ICE, by means of a comparison of the simulated results and the experimental measurements detected under real operating conditions has highlighted the most accurate mathematical models for the prediction of the dynamic thermal behaviour of the STB in the absence and presence of a PCM.

TL;DR: A framework that utilizes the generative adversarial network (GAN) to address the imbalanced data problem in FDD for air handling units (AHUs) and demonstrates the promising prospects of performing robust FDD of AHU with a limited number of faulty training samples.

TL;DR: An overview of the available literature on Life Cycle Energy, Life Cycle Greenhouse gasses, and Conventional Life Cycle Assessment of commercial and residential buildings was presented with respect to their height as discussed by the authors.

TL;DR: A literature review on studies using machine learning (ML) models to predict occupancy and window-opening behaviour and their application in smart buildings finds that the energy consumption of heating, ventilation and air-conditioning systems can be reduced by 23% on average by optimizing the control strategy of HVAC systems according to the occupancy information predicted by the ML models.

TL;DR: Using data from 47 commercial buildings, a number of machine learning algorithms were evaluated to predict the electricity demand at individual building level and aggregated level in hourly intervals and showed that boosted-tree, random forest, and ANN provided the best outcomes for prediction at hourly granularity.

TL;DR: In this paper, the authors summarize the integrated system configurations in the building environment from both scientific researches and practical applications, and identify the system characteristics and suitable application area, focusing on thermal comfort and indoor air quality and critical design parameters that have impacts on these two aspects, such as location and amount of heat sources, contaminant source types, room geometry, and condensation risk.

TL;DR: A comprehensive and in-depth systematic review of the up-to-date literature related to the application and characterization of ANN-based metamodels for BPS.

TL;DR: The application of active archive non-dominated sorting genetic algorithm (aNSGA-II) towards multi-objective optimization is illustrated and the optimal solution in the R4 space is discussed, alongside with considerations about the solutions pertaining to the Pareto frontier.

TL;DR: While GA-based MOO's robust evaluation for supporting building retrofit and its DM process needs further research, promising potential is shown overall, when complemented with auxiliary techniques.

TL;DR: In this paper, the authors performed an extensive numerical investigation on the integration of phase change materials (PCM) into building walls to establish the key conditions required for effective utilization of PCM in reducing heat gains in the cooling season and heat losses in the heating season.

TL;DR: In this article, a combined energy and seismic retrofitting is investigated across twenty European cities with varied seismic hazard levels and different climatic conditions, and it is found that a combined retrofitting scheme will reduce substantially the payback periods in moderate to high seismicity regions.

TL;DR: In this article, the authors developed and evaluated eight heat mitigation scenarios based on the use of reflective surfaces, additional greenery, an increase in the evapotranspiration rate and several combinations of these factors.

TL;DR: Building efficiency design needs to be conducted from a systematic view to figure out the underlying issues among different efficient design measures, and energy performance benchmarking of both occupant behavior interventions and the technological updates and building service systems are urgently needed to help the building users to make smart decisions.

TL;DR: In this article, personal comfort systems (PCS) offer the occupants the choice of modulating their immediate thermal ambience with local controls, which helps in improving the subjective thermal and air quality acceptability with the desired thermal sensation.