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During the pre-design stage of buildings, reliable long-term prediction of thermal loads is significant for cooling/heating system configuration and efficient operation. This paper proposes a surrogate modeling method to predict all-year hourly cooling/heating loads in high resolution for retail, hotel, and office buildings. 16 384 surrogate models are simulated in EnergyPlus to generate the load database, which contains 7 crucial building features as inputs and hourly loads as outputs. K-nearest-neighbors (KNN) is chosen as the data-driven algorithm to approximate the surrogates for load prediction. With test samples from the database, performances of five different spatial metrics for KNN are evaluated and optimized. Results show that the Manhattan distance is the optimal metric with the highest efficient hour rates of 93.57% and 97.14% for cooling and heating loads in office buildings. The method is verified by predicting the thermal loads of a given district in Shanghai, China. The mean absolute percentage errors (MAPE) are 5.26% and 6.88% for cooling/heating loads, respectively, and 5.63% for the annual thermal loads. The proposed surrogate modeling method meets the precision requirement of engineering in the building pre-design stage and achieves the fast prediction of all-year hourly thermal loads at the district level. As a data-driven approximation, it does not require as much detailed building information as the commonly used physics-based methods. And by pre-simulation of sufficient prototypical models, the method overcomes the gaps of data missing in current data-driven methods. [ABSTRACT FROM AUTHOR]

This paper proposes a meta-modeling workflow to forecast the cooling and heating loads of buildings at individual and district levels in the early design stage. Seven input variables, with large impacts on building loads, are selected for designing meta-models to establish the MySQL database. The load profiles of office, commercial, and hotel models are simulated with EnergyPlus in batches. A sequence-to-sequence (Seq2Seq) model based on the deep-learning method of a one-dimensional convolutional neural network (1D-CNN) is introduced to achieve rapid forecasting of all-year hourly building loads. The method performs well with the load effective hour rate (LEHR) of around 90% and MAPE less than 10%. Finally, this meta-modeling workflow is applied to a district as a case study in Shanghai, China. The forecasting results well match the actual loads with R2 of 0.9978 and 0.9975, respectively, for the heating and cooling load. The LEHR value of all-year hourly forecasting loads is 98.4%, as well as an MAPE of 4.4%. This meta-modeling workflow expands the applicability of building-physics-based methods and improves the time resolution of conventional data-driven methods. It shows small forecasting errors and fast computing speed while meeting the required precision and convenience of engineering in the building early design stage. [ABSTRACT FROM AUTHOR]

PV windows are considered to be a promising building-integrated PV technology due to their excellent electrical, daylighting and thermal performance. However, the shading of the PV cells increases the heating load in winter and the high temperature of PV cells exacerbates the cooling load in summer. Moreover, a significant portion of the heat absorbed by the PV modules is usually wasted. To address these issues, this paper proposed a multi-functional PV/T window. This system could recover the heat on PV cells for producing warm air in winter and hot water in other seasons. As a result, the proposed system can realize the combined PV/T utilization of solar energy and reduce the seasonal thermal needs of the building. First, the mathematical model of the proposed window was developed and validated. Next, the developed model was integrated into building energy software to evaluate the overall energy performance of the proposed window in four different cities of Yangtze River region: Shanghai, Nanjing, Wuhan, and Chongqing in China. These cities experience the subtropical humid climate (Cfa in the Köppen-Geiger classification) and belong to the zones with medium global horizontal irradiation. The sensitivity analysis revealed that the optimal slat angles are between 0o and 40o, while the optimal orientation angles range from 30o to 60o. Selecting low-iron glass as the inner glass resulted in the lowest overall energy consumption. By adopting the MFPV/T window, the annual operation cost of the studied case could be reduced by 34% (6.4 $/m2), 36% (7.0 $/m2), 28% (5.7 $/m2), and 16% (2.9 $/m2) in these four cites, respectively. Additionally, the annual CO 2 emissions could be curtailed by 53.5, 58.5, 47, and 22.3 kg/m2, respectively. • A novel PV/T window was proposed for addressing the common issues of PV windows. • The mathematical model of proposed window was developed and validated. • The parameter sensitivity was analyzed. • The overall energy performance was predicted in Yangtze River region. [ABSTRACT FROM AUTHOR]

• The H 2 -O 2 PEMFC adopts a DEAC operation mode to achieve 100 % utilization of reactants. • PEMEC-PEMFC achieves self-sufficiency of reactants with water self-supply. • The number of PEMEC varies according to solar radiation and light time. • The hourly loads of households can be satisfied by adopting the pre-operation mode. Targeting the shortcomings of oxygen waste and reactant supplement of electrolyzer, this paper proposes a novel combined cooling, heating and power (CCHP) system based on proton exchange membrane electrolysis cell (PEMEC) and H 2 /O 2 proton exchange membrane fuel cell (PEMFC) with water self-supply. In this system, H 2 /O 2 PEMFC adopts a dead-ended anode and cathode operation mode to achieve 100 % hydrogen and oxygen utilization and water recovery. For the application scenario of a household in Shanghai, China, the performance of the CCHP system in summer and winter is investigated. When the system gives priority to meeting the electric and cooling load, there will be insufficient heat at the initial stage of operation. The pre-operation model can realize the hourly matching of residential electric, heating and cooling loads. After one day of operation in summer and winter, there are 19.1 kWh and 45.5 kWh electricity surplus, 7.27 × 106 kJ and 1.97 × 106 kJ heat surplus, 1200 mol and 300 mol hydrogen surplus, respectively. Moreover, the estimated payback period for this system is 7.6 years, which will be significantly shortened with the decrease of the cost of photovoltaic panels and PEMEC. [ABSTRACT FROM AUTHOR]

The building energy consumption and energy saving rate in office buildings with Dye‐doped liquid crystal (DLC) smart window under the change of region, window‐wall ratio and orientation were simulated by DeST‐C. The results indicated that the energy saving rate of DLC smart window increased with the rising proportion of cooling load in different regions: Harbin < Beijing < Shanghai < Guangzhou. When DLC smart window was used in Shanghai, the energy saving rate increased with the increase of window‐wall ratio. Moreover, DLC smart window in different orientations generally resulted in different energy saving rate: North < South< East