Evacuated tube solar collectors (ETSCs) have gained a significant share of the solar thermal collector (STC) market. Compared to other collector types, ETSCs cover a relatively wide range of operating temperatures, mostly offer higher thermal efficiency, and are available at reasonable prices. But similar to other solar energy technologies, ETSCs are suffering from two main drawbacks associated with intermittency of solar radiation. Phase change materials (PCMs) have been widely used to overcome this challenge. If properly designed and utilised, PCMs can reduce the energy fluctuations and store the solar thermal energy during the daytime and release it in the absence of sunlight. There have been many studies conducted on the integration of PCMs with ETSCs, but the lack of a comprehensive systematic review study focused on such integrated energy systems has remained to be a gap in the literature. This gap is addressed by the present review study. Based on both theoretical and experimental results reported in the literature, the present study focuses on PCM assisted ETSC systems from different perspectives such as integration types, design parameters, and performance. Four main types of integration between PCMs and ETSCs are identified and advantages and disadvantages of each type compared to the others are discussed. Furthermore, state of the art is also clarified, the knowledge gaps are identified, and a roadmap for further studies on these energy systems is provided accordingly.

On the integration of phase change materials with evacuated tube solar thermal collectors

Clean air is critical component for health and survival of human and wildlife, as atmospheric pollution is associated with a number of significant diseases including cancer. However, due to rapid industrialization and population growth, activities such as transportation, household, agricultural, and industrial processes contribute to air pollution. As a result, air pollution has become a significant problem in many cities, especially in emerging countries like India. To maintain ambient air quality, regular monitoring and forecasting of air pollution is necessary. For that purpose, machine learning has emerged as a promising technique for predicting the Air Quality Index (AQI) compared to conventional methods. Here we apply the AQI to the city of Visakhapatnam, Andhra Pradesh, India, focusing on 12 contaminants and 10 meteorological parameters from July 2017 to September 2022. For this purpose, we employed several machine learning models, including LightGBM, Random Forest, Catboost, Adaboost, and XGBoost. The results show that the Catboost model outperformed other models with an R2 correlation coefficient of 0.9998, a mean absolute error (MAE) of 0.60, a mean square error (MSE) of 0.58, and a root mean square error (RMSE) of 0.76. The Adaboost model had the least effective prediction with an R2 correlation coefficient of 0.9753. In summary, machine learning is a promising technique for predicting AQI with Catboost being the best-performing model for AQI prediction. Moreover, by leveraging historical data and machine learning algorithms enables accurate predictions of future urban air quality levels on a global scale.

Air quality prediction by machine learning models: A predictive study on the indian coastal city of Vishakhapatnam.

Human-building interaction for indoor environmental control: Evolution of technology and future prospects

Environmental-thermal-economic performance trade-off for rural residence retrofitting in the Beijing–Tianjin–Hebei region, Northern China: A multi-objective optimisation framework under different scenarios

https://onlinelibrary.wiley.com/doi/epdf/10.1002/est2.103

Thermodynamic analysis of a hybrid energy system using geothermal and solar energy sources with thermal storage in a residential building

The proper application of machine learning and genetic algorithms in the early stage of library design can obtain better all-around building performance. The all-around performance of the library, such as indoor temperature, solar radiation, indoor lighting, etc., must be fully considered in the initial design stage. Aiming at building performance optimization and based on the method of “generative design”, this paper constructs the library’s comprehensive performance evaluation workflow and rapid prediction combined with the LightGBM algorithm. A library in a cold region of China is taken as the research object to verify its application. In this study, 5000 scheme samples generated in the iterative genetic optimization process were taken as data sets. The LightGBM algorithm was used to classify and predict design schemes, with a precision of 0.78, recall rate of 0.93, and F1-Score of 0.851. This method can help architects to fully exploit the optimization potential of the building’s all-around performance in the initial stage of library design and ensure the timely interaction and feedback between design decisions and performance evaluation.

/pdf/application-of-lightgbm-algorithm-in-the-initial-design-of-a-qk9i8h5a.pdf

Application of LightGBM Algorithm in the Initial Design of a Library in the Cold Area of China Based on Comprehensive Performance

Framework on low-carbon retrofit of rural residential buildings in arid areas of northwest China: A case study of Turpan residential buildings

Study on optimization method of summer nature ventilation for residential buildings in typical thermal zone of Xinjiang, China

Net-zero energy buildings (NZEB) have received widespread attention for their excellent energy and carbon reduction potential in various countries. However, relatively little research has been conducted on the life performance of its primary form: the ultra-low energy building (ULEB). This paper proposes an optimization method combining meta-models to investigate the carbon reduction potential of ultra-low energy buildings in severely cold regions of China. The XGBoost algorithm is used to construct a meta-model of building performance, and the grid search method is used to obtain a high-precision meta-model with an R2 of 0.967. Secondly, NSGA-II is used to find passive technical solutions based on the meta-model that minimize the global warming potential (GWP), global cost (GC), and operation energy consumption (OE). Finally, the variables affecting the life-cycle performance of buildings were ranked by sensitivity analysis. The results show that GWP, GC, and OE are reduced by 12.7%, 6.7%, and 7.4% compared with the original building through the optimization process proposed. Sensitivity analysis showed that for GWP, the top four sensitivities are window type (TW) > WWR of south wall (WWRS) > roof insulation thickness (IR) > WWR of north wall (WWRN). For GC, the top four sensitivities are: TW > WWRS > IR > WWR of west wall (WWRW); for OE, the top four sensitivities are: TW > IR > WWRS > WWRN. This paper’s optimization framework and research results can effectively guide the design of the ULEB in severely cold regions.

/pdf/multi-objective-optimization-of-ultra-low-energy-consumption-190n62u6.pdf

Multi-Objective Optimization of Ultra-Low Energy Consumption Buildings in Severely Cold Regions Considering Life Cycle Performance

Wall-mounted solar chimneys use solar radiation to heat the air inside the chimney cavity and use thermal pressure to create natural convection. Applying this principle allows for the indoor ventilation of a building without energy consumption. However, in wall-mounted solar chimney designs, different air inlet and outlet design dimensions can have varying degrees of impact on the effectiveness of wall-mounted solar chimney ventilation. In order to analyze the internal airflow state and airflow temperature field distribution of wall-mounted solar chimneys, physical models of wall-mounted solar chimneys with six different air outlet-to-inlet cross-sectional area ratios were developed in this research work. Before numerical simulation analysis, heat transfer analysis of the wall-mounted solar chimney’s structural components and airflow channels was carried out, and corresponding mathematical heat transfer models were established. The internal flow state and temperature distribution characteristics of a wall-mounted solar chimney were analyzed by steady-state simulations using the computational fluid dynamics software, Ansys Fluent. Finally, transient simulation calculation analysis was conducted under six different S-value models to investigate the variation in the natural ventilation of a single-story building’s wall-mounted solar chimney for a whole day. The study showed that under the same simulation conditions, 80% ≤ S < 100% effectively avoided the formation of vortices in the internal airflow of the wall-mounted solar chimneys and kept the ventilation effect of wall-mounted solar chimneys at a high level. The results of this study provide a reference for the optimization of research on the design of the air inlet and outlet structures of wall-mounted solar chimneys for single-story buildings.

/pdf/structural-and-heat-transfer-model-analysis-of-wall-mounted-qcscs625.pdf

Structural and Heat Transfer Model Analysis of Wall-Mounted Solar Chimney Inlets and Outlets in Single-Story Buildings

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