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Compared with traditional filter paper, a high-purity cellulose membrane prepared via a green pathway was used to enhance detection performance for cadmium ions. In this work, a simple, rapid, and ...

Buildings now incorporate increasing levels of renewable energy to the point where net zero energy buildings (NetZEBs) and net zero energy clusters (NetZECs) have the potential to become widely used. Information exchange among stakeholders is essential to enable energy sharing among buildings that form a NetZECs. However, traditional, centralised energy management usually places emphasis on the supply side and does not address data exchange among individual stakeholders. This paper proposes an information sharing strategy based on linked data for improved management of NetZEBs and NetZECs. This strategy systematically integrates stakeholders' engagement by using energy performance indicators as information carriers and linked data as engagement channels. A case study proves the advantages of the developed strategy for improving the energy balance at individual building level, building cluster level, and community level. It is found that the strategy could help achieve NetZEBs and NetZECs through empowering stakeholders' interaction and information sharing.

The prediction of energy consumption is important for the efficient operation of building air-conditioning systems. Most predicted models are based on historical energy consumption data and the factors influencing air conditioning systems, including weather, time of day, and previous consumption. However, the traditional prediction models, such as the Autoregressive Integrated Moving Average (ARIMA) time series model and back propagation (BP) neural network model, show large errors in their prediction of the energy consumption of air-conditioning systems. To achieve better prediction, the Long Short-Term Memory (LSTM) model of deep learning is adopted in this study based on an air-conditioning system of a University Library in Guangzhou. The results demonstrate that the LSTM model can produce more reliable predictions. The daily energy consumption forecast reduced by 11.2 % compared to that of the Autoregressive Moving Average model (MAPE). The hourly energy consumption forecast reduced by 16.31 %. In addition, compared with the BP neural network model, the MAPE’s daily energy consumption prediction reduced by 49 % and the hourly energy consumption prediction reduced by 36.61 %.

In this study, a new two-dimensional model of CPRF was developed in TRNSYS. Its accuracy was validated by a full-scale experiment. The effects of key parameters of CPRF coupled with solar water heating system on indoor temperature was analyzed. The single factor analysis revealed that reducing tube pitch and solidification temperature, increasing PCM layer thickness and solar collector area could increase indoor temperature. The orthogonal test analysis indicated that tube pitch has the greatest impact on the average indoor temperature. The CPRF heating system coupled to solar energy and an electric boiler operating on valley electricity (CSSEBV) was proposed to ensure stable heating. It was optimized by GENOPT. The optimal parameters are that the tube pitch is 0.28 m, the PCM layer thickness is 0.02 m, the solar collector area is 12.5 m2 and the operating time of the electric boiler is 2h. The economy, energy saving property and environmental benefits of four systems were also compared and analyzed.

Due to energy crisis and phenomenon of global warming, green buildings have been undergoing vigorous development in China, and people are paying increasing attention to the actual operational effect of green buildings rather than the design. In this paper, 12 high-star green buildings (3 LEED Gold buildings, 1 operation Two-Star building, 1 operation Three-Star building, 1 design Two-Star building, and 6 design Three-Star buildings) in the cold region and hot summer and warm winter region (HSWW region) were taken as the research objects to evaluate their indoor thermal comfort. First, from December 2016 to March 2018, this paper conducted a continuous collection of indoor objective thermal environment data of green buildings to obtain the actual thermal environment distribution, and proposed new indexes to evaluate indoor thermal environment of green buildings, namely, OTCD (Over Thermal Comfort Distance) and OTCP (Over Thermal Comfort Percentage). It was found that indoor temperature and humidity of most buildings didn't reach the standard. The new evaluation indexes clearly showed the percentage of the indoor temperature and humidity of each green building away from the comfort zone under different standards in mathematical form, which is convenient for comparison. Second, a questionnaire survey on people's subjective satisfaction of indoor thermal comfort in green buildings was carried out at the same time, wherein 1400 valid questionnaires were recovered. Based on the different evaluation results of subjective and objective thermal comfort, the operative comfort temperature/humidity and thermal/humid comfort zone of different building climate zones were obtained by cluster and linear regression analysis. The results showed that there are differences between thermal/humid comfort zone of different building climate zones and it is helpful for the research on thermal comfort of green buildings in China and the development of relevant codes.