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This paper proposes a quantity-quality-based optimization method of indoor thermal environment design that emphasizes entransy and exergy analysis. We scrutinized the different focuses of entransy and exergy in examining an energy-related phenomenon or process, and pointed out the need for integrating entransy and exergy for the optimization of indoor thermal environment design. The proposed method contributes to identifying the most energy-efficient solution for attaining the same level of indoor thermal comfort for end users by quantifying the entransy and exergy efficiency of active technologies. With this method, a benchmark technical solution was properly determined and benchmarks for entransy dissipation and exergy loss during the process of thermal environment design were quantified. Entransy dissipation and exergy loss under common technologies were compared with the benchmark values. The concepts of relative entransy savings and relative exergy savings were defined as the evaluation indexes of technical energy efficiency. Referencing winter indoor thermal environment design for residential buildings in hot-summer and cold-winter (HSCW) regions in China, the proposed method was applied to assess the energy efficiency of different heating methods, including an inverter air conditioner, an “air source heat pump + floor radiation,” a “wall-hanging gas heater + floor radiation,” a “wall-hanging gas heater + radiator,” and an oil-filled radiator. This paper recommended that the “air source heat pump + floor radiation” be used for residential buildings in winter in HSCW regions to improve energy efficiency. In addition, the optimization results of the proposed method were compared with that of traditional energy and exergy analysis methods. The results showed that the new method more accurately analyzed the energy flow in indoor thermal environment design, and therefore can serve as an improved way of thinking about follow-up studies on the optimization of heat pump units and the operation strategies of floor radiant heating systems.

This research investigates sustainable building design from a new perspective – green design in relation to green building rating systems. We examine the potential influence of Green Star (GS) in Australia on the design of a project and compare this with the Leadership in Energy and Environmental Design (LEED) in America and Assessment Standard for Green Buildings (ASGB) in China. The comparison is conducted using a typical case study of a GS Six Stars certified example, the Melbourne School of Design building. A critical review concludes that LEED and ASGB are design-guide schemes while GS is a performance-based rating system and this affects the project outcome. We note that LEED is oriented to energy efficiency while GS and ASGB holistically consider energy and indoor environment quality. GS, additionally, emphasizes project process management. Potential LEED and ASGB certification levels for the case are calculated; possible changes to the design are indicated to achieve the highest LEED and ASGB levels. The predicted results demonstrate the influence of different environmental concerns and assessment approaches of the three on the green design and the performance of buildings themselves. Based on this finding, the paper argues that a performance-based rating system (e.g. GS) is more beneficial to the practice of designing green, compared with other measure-based systems.

Energy consumption in machining contributes a significant part of manufacturing cost and produces a great environmental impact. This paper provides a critical assessment on energy consumption in a machining system. Energy consumption is classified at the process, machine, and system levels. Machine tool power demand at different machine states by different components is also discussed. Then predictive methods of energy consumption at different levels are summarized. Finally, energy consumption reduction strategies are discussed to achieve sustainable manufacturing.

This paper examines naturally ventilated buildings in hot and humid summer zones and proposes an air enthalpy-based energy conservation rating method with an emphasis on the combined thermal comfort-ventilation parameters, particularly the impact of humidity and human adaptations on thermal comfort. The new method starts with energy flow analysis to a naturally ventilated room and assessment of thermal comfort accounting for the humidity of the naturally ventilated room as well as the occupants’ adaptability, differing from the PMV models and widely-used adaptive models adopted in existing rating methods. It contributes to designing a well-performed naturally ventilated building by analysing the interplay of climate elements, design features, indoor thermal comfort, and energy consumption for cooling in hot-humid climates. It also gives the access to rate the influence of an estimated energy saving due to natural ventilation on the energy system at a district or national scale. The proposed method is then applied to a naturally ventilated office located in three cities within this particular climatic region of China. The results indicate that natural ventilation is an effective way to improve thermal comfort while maintaining a low cooling energy consumption in hot-humid summer zones. Using natural ventilation could help reduce cooling energy demand by 10–30% compared to not using natural ventilation. Its energy saving potential is strongly affected by the enthalpy of outdoor air, followed by airflow rate. Then, a contrast comparison between the new method based on energy balance and Chinese indoor thermal comfort standard and the conventional method coupling adaptive ASHRAE standard-55 thermal comfort model with sensible heat balance model is carried out. The contrast results validate the considerable impacts of humidity on energy balance analysis and thermal comfort rating. It points out the new method makes improvement of the maximum energy saving potential of naturally ventilated buildings prediction.

The study proposes a nonparametric approach that uses machine learning to predict outdoor temperatures based on field measurements at the National University of Singapore (NUS) Kent Ridge campus from February 2019 to July 2019. Six urban morphology variables (e.g. BDG, PAVE, WALL, HBDG, SVF, GnPR) were extracted from geographical information system (GIS) maps, three dimensional (3D) model and field surveys. This study compares the predictive power between ordinary least squares linear regression (LR) and machine learning (e.g. random forest (RF)). By using RF as a regression model, the air temperature has a greater RMSE reduction than LR, ranging from 10 % to 33 %. The relationship between outdoor air temperature and urban morphology variables based on non-parametric regression is presented. On average, lower SVF space can reduce the heat on campus. As the WALL increases, the temperature rises, but the changes in day and night are different. Greenery reduces daytime temperature due to the shading of solar radiation, although the shading from trees blocks the release of long-wave radiation from artificial materials at night. The observation indicates nighttime temperature can be reduced by planting low height greenery while keeping SVF unchanged.

We analyzed the ASHRAE Global Thermal Comfort Database II to answer a fundamental but overlooked question in thermal comfort studies: how many and which subjective metrics should be used for the assessment of the occupants' thermal experience. We found that the thermal sensation is the most frequently used metrics in Thermal Comfort Database II, followed by thermal preference, comfort and acceptability. The thermal sensation/thermal preference, thermal comfort/air movement acceptability and thermal comfort/thermal preference are the top three most dependent metrics pairs. A principal component analysis confirmed that the personal experience of thermal conditions in built environment is not a one-dimensional problem, but at least a two-dimensional problem, and suggested thermal sensation and thermal comfort should be asked in right-now surveys as the first two Principal Component are majorly constructed by thermal sensation and thermal comfort. To further confirm the predictive power of thermal sensation and comfort, we used logistic regression and support vector machine to predict thermal acceptability and thermal preference with thermal sensation and comfort. The prediction accuracy is 87% for thermal acceptability and 64% for thermal preference. The prediction error might be due to occupants' individual difference and people errors in answering survey. These findings could help the design of chamber experiments, field studies, and human-building interaction interfaces by shedding light on the choice of subjective thermal metrics to effectively and accurately collect information on occupants’ thermal experience.

s Despite the extensive literature on solar-assisted heat pump systems, including optimizations of systems configurations and operation strategies, there are few works of literature on the potential of applying solar thermal-air source heat pump water heaters (SA-WH) in solar shortage regions and none that compares alternative system improvements for the same boundary conditions and with the same level of comfort to the end-user. This work examines the feasibility of the SA-WH in solar shortage regions from a performance perspective and compares its performance to the solar water heater (SWH) and the air-source heat pump water heater (ASHP-WH). Experimental studies were carried out from March 22, 2014 to April 21, 2014, which covered spring-summer transition seasons of these particular areas. The parameter η was proposed and a reference ηbase of 2.4 was defined to rate the performance of these three systems. The results showed nearly 80% of working conditions of the SA-WH had a η which was higher than 2.4, wherein 70% were above 3. Furthermore, this study presents a regression model verified by measured data to predict the potential performance of the SA-WH in renewable energy shortage regions. This prediction model provides an optimal method of fully applying solar energy for the domestic hot water (DHW) and even heating by targeting better system performance. Then, three solar energy shortage cites of China were used as cases to illustrate the use of the prediction model. To meet the DHW demand in transitional seasons of these regions, the air-source heat pump of the SA-WH is suggested to be switched on 1 h before using when the average ambient temperature is below 20 °C; and be switched on at the hour with the highest temperature of the day when the average ambient temperature is above 20 °C and operate 1 h.