Ximin Cao^*, Xinglong Chen, He Huang, Yanchi Zhang, Qifan Huang

Energy Engineering, Vol.121, No.4, pp. 1067-1089, 2024, DOI:10.32604/ee.2023.046783

Abstract Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals. Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system, a multi-time scale optimal scheduling strategy based on model predictive control (MPC) is proposed under the consideration of load optimization. First, load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature, and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost. Second, considering inter-day to… More >

Pu Zhang¹, Yingjun Li¹, Xinyu Zhu¹, Shizhan Xu¹, Pinwu Guan^1,*, Wei Liu², Yanwei Guo², Haibo Wang²

Structural Durability & Health Monitoring, Vol.18, No.2, pp. 143-159, 2024, DOI:10.32604/sdhm.2024.046986

Abstract Grouting defects are an inherent challenge in construction practices, exerting a considerable impact on the operational structural integrity of connections. This investigation employed the impact-echo technique for the detection of grouting anomalies within connections, enhancing its precision through the integration of wavelet packet energy principles for damage identification purposes. A series of grouting completeness assessments were meticulously conducted, taking into account variables such as the divergent material properties of the sleeves and the configuration of adjacent reinforcement. The findings revealed that: (i) the energy distribution for the high-strength concrete cohort predominantly occupied the frequency bands 42, 44, 45, and 47,… More >

Noureddine Abouricha^1,*, Ayoub Gounni², Mustapha El Alami²

Frontiers in Heat and Mass Transfer, Vol.22, No.1, pp. 359-375, 2024, DOI:10.32604/fhmt.2024.047632

Abstract In this paper, experimental and numerical studies of heat transfer in a test local of side heated from below are presented and compared. All the walls, the rest of the floor and the ceiling are made from plywood and polystyrene in sandwich form ( plywood- polystyrene- plywood) just on one of the vertical walls contained a glazed door (). This local is heated during two heating cycles by a square plate of iron the width , which represents the heat source, its temperature is controlled. The plate is heated for two cycles by an adjustable set-point heat source placed just… More >

Perez Joan¹, Fusco Giovanni¹, Sadahiro Yukio²

Revue Internationale de Géomatique, Vol.31, No.2, pp. 303-327, 2022, DOI:10.3166/RIG.31.303-327© 2022

Abstract This paper presents different methods implemented with the aim of studying urban dynamics at the building level. Building types are identified within a comprehensive vector-based building inventory, spanning over at least two time points. First, basic morphometric indicators are computed for each building: area, floor-area, number of neighbors, elongation, and convexity. Based on the availability of expert knowledge, different types of classification and clustering are performed: supervised tree-like classificatory model, expert-constrained k-means and combined SOM-HCA. A grid is superimposed on the test region of Osaka (Japan) and the number of building types per cell and for each period is computed,… More >

Alessandro Araldi¹, David Emsellem², Giovanni Fusco¹, Andrea Tettamanzi³, Denis Overal²

Revue Internationale de Géomatique, Vol.31, No.2, pp. 265-302, 2022, DOI:10.3166/RIG.31.265-302© 2022

Abstract The identification and description of building typologies play a fundamental role in the understanding of the overall built-up form. A growing body of research is developing and implementing sophisticated, computer-aided protocols for the identification of building typologies. This paper shares the same goal. An innovative data-driven procedure for the unsupervised identification and description of building types and organization is here presented. After a specific pre-processing procedure, we develop an unsupervised clustering combining a new algorithm of Naive Bayes inference and hierarchical ascendant approaches relying on six morphometric features of buildings. This protocol allows us to identify groups of buildings sharing… More >

Ming Liu¹, Pinghua Zhu^2,*, Xiancui Yan², Haichao Li², Xintong Chen²

Journal of Renewable Materials, Vol.12, No.2, pp. 329-347, 2024, DOI:10.32604/jrm.2023.045381

Abstract As socioeconomic development continues, the issue of building energy consumption has attracted significant attention, and improving the thermal insulation performance of buildings has become a crucial strategic measure. Simultaneously, the application of solid waste in insulation materials has also become a hot topic. This paper reviews the sources and classifications of solid waste, focusing on research progress in its application as insulation materials in the domains of daily life, agriculture, and industry. The research shows that incorporating household solid waste materials, such as waste glass, paper, and clothing scraps into cementitious thermal insulation can significantly reduce the thermal conductivity of… More >

Zhengrong Shi^1,3, Jie Ren¹, Tao Zhang^1,3,*, Yanming Shen^2,*

Energy Engineering, Vol.121, No.3, pp. 681-702, 2024, DOI:10.32604/ee.2023.045238

Abstract Building energy consumption and building carbon emissions both account for more than 20% of their total national values in China. Building employing phase change material (PCM) for passive temperature control shows a promising prospect in meeting the comfort demand and reducing energy consumption simultaneously. However, there is a lack of more detailed research on the interaction between the location and thickness of PCM and indoor natural convection, as well as indoor temperature distribution. In this study, the numerical model of a passive temperature-controlled building integrating the developed PCM module is established with the help of ANSYS. In which, the actual… More > Graphic Abstract

Ahmad Almadhor¹, Chitapong Wechtaisong^2,*, Usman Tariq³, Natalia Kryvinska^4,*, Abdullah Al Hejaili⁵, Uzma Ghulam Mohammad⁶, Mohana Alanazi⁷

Computer Systems Science and Engineering, Vol.48, No.1, pp. 199-216, 2024, DOI:10.32604/csse.2023.039546

Abstract Thermal comfort is an essential component of smart cities that helps to upgrade, analyze, and realize intelligent buildings. It strongly affects human psychological and physiological levels. Residents of buildings suffer stress because of poor thermal comfort. Buildings frequently use Heating, Ventilation, and Air Conditioning (HVAC) systems for temperature control. Better thermal states directly impact people’s productivity and health. This study revealed a human thermal comfort model that makes better predictions of thermal sensation by identifying essential features and employing a tuned Extra Tree classifier, MultiLayer Perceptron (MLP) and Naive Bayes (NB) models. The study employs the ASHRAE RP-884 standard dataset… More >

Noman Khan^1,2, Samee Ullah Khan^1,2, Sung Wook Baik^1,2,*

Computer Systems Science and Engineering, Vol.48, No.1, pp. 153-173, 2024, DOI:10.32604/csse.2023.039407

Abstract Energy management systems for residential and commercial buildings must use an appropriate and efficient model to predict energy consumption accurately. To deal with the challenges in power management, the short-term Power Consumption (PC) prediction for household appliances plays a vital role in improving domestic and commercial energy efficiency. Big data applications and analytics have shown that data-driven load forecasting approaches can forecast PC in commercial and residential sectors and recognize patterns of electric usage in complex conditions. However, traditional Machine Learning (ML) algorithms and their features engineering procedure emphasize the practice of inefficient and ineffective techniques resulting in poor generalization.… More >

Shan Xu^1,*, Huadu Tang¹, Ding Wang¹, Ruiguang Zhu¹, Liwei Wang¹, Shengwang Hao¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.4, pp. 1-3, 2023, DOI:10.32604/icces.2023.09930

Abstract Damage evaluation is an important index for the evaluation of buildings health. To provide a rapid crack evaluation in practical applications, a crack identification and damage evaluation via deep learning framework is proposed in this paper. We built a combined dataset from Kaggle and site photos. A pre-trained U-net model is used to perform the training of model. With updated weights, the identification of cracks could be performed on non-labelled photos. More >