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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.

Pre-dehumidification time (τpre) and pre-dehumidification energy consumption (Epre) play important roles in preventing the condensation of moisture on the floors of rooms that use a radiant floor cooling (RFC) system. However, there are few theoretical or experimental studies that focus on these two important quantities. In this study, an artificial neural network (ANN) was used to predict condensation risk for the integration of RFC systems with mixed ventilation (MV), stratum ventilation (SV), and displacement ventilation (DV) systems. A genetic algorithm-back-propagation (GA-BP) neural network model was established to predict τpre and Epre. Both training data and validation data were obtained from tests in a computational fluid dynamics (CFD) simulation. The results show that the established GA-BP model can predict τpre and Epre well. The coefficient of determination (R2) of τpre and of Epre were, respectively, 0.973 and 0.956. For an RFC system integrated with an MV, SV, or DV system, the lowest values of τpre and Epre were with the DV system, 23.1 s and 0.237 kWh, respectively, for a 67.5 m3 room. Therefore, the best pre-dehumidification effect was with integration of the DV and RFC systems. This study showed that an ANN-based method can be used for predictive control for condensation prevention in RFC systems. It also provides a novel and effective method by which to assess the pre-dehumidification control of radiant floor surfaces.

Solar lighting technologies can lead sunlight into indoor actively, which has been proved that has great potential to reduce electric lighting consumption and create a healthy visual environment. In this paper, a mathematics model was developed to explore performance characteristics and economic applicability of solar lighting/heating system that combine ordinary solar lighting system with nanofluids to spectroscopically utilize sunlight. The results show that the luminous efficiency of output visible light can reach at 242 lm/W, which is 1.62–2.42 times higher than that of current LED light. Moreover, it can be found that the energy-saving capacity of this system is remarkable when used in most areas of China. As for Harbin, its annual total solar radiation ranges 4190 MJ/m2 to 5020 MJ/m2, the system's annual output energy (per square meter of collection area) is 188.15 kW•h for daylighting and 248.2 kW•h for domestic hot water. Besides, the integrated using of infrared radiation can improve the economy of solar lighting technologies by calculating the comprehensive price of unit output energy.

Shopping complexes are widely built for their convenience and multiple functions. However, their complex functional areas, result in significantly different thermal environments and various customers’ thermal perception. To explain the influence of functional related parameters on the thermal perception of customers in shopping complexes, we selected two typical functional related parameters, including customers’ thermal expectation level of indoor environment in their current area and the area where the customer was ten minutes before taking the survey. 851 valid questionnaires were obtained in two typical shopping complexes during July. Customers’ thermal neutral temperature, thermal preference temperature and thermal comfort temperature range were calculated in different functional areas. Customers’ thermal expectation level was quantified by using expectancy factor. The results showed that customers’ thermal expectation level in entertainment areas was the highest, followed by food courts, retail areas, and transition spaces. Customers’ thermal expectation level would influence their thermal neutral temperature and thermal sensitivity. Customers with different thermal experience differed significantly in their thermal sensation voting (p< 0.01). The highest thermal sensitivity, about 0.41/°C, was found in customers moving from high-temperature areas to low-temperature areas. These findings help to clarify how functional related parameters affect the thermal comfort of customers and provide the guidance for designing the indoor temperature in shopping complexes.

Internet of Things (IoT) technologies are increasingly implemented in buildings as the cost-effective smart sensing infrastructure of building automation systems (BASs). They are also dispersed computing resources for novel distributed optimal control approaches. However, wireless communication networks are critical to fulfill these tasks with the performance influenced by inherent uncertainties in networks, e.g., unpredictable occurrence of link failures. Centralized and hierarchical distributed approaches are vulnerable against link failure, while the robustness of fully distributed approaches depends on the algorithms adopted. This study therefore proposes a fully distributed robust optimal control approach for air-conditioning systems considering uncertainties of communication link in IoT-enabled BASs. The distributed algorithm is adopted that agents know their out-neighbors only. Agents directly coordinate with the connected neighbors for global optimization. Tests are conducted to test and validate the proposed approach by comparing with existing approaches, i.e., the centralized, the hierarchical distributed and the fully distributed approaches. Results show that different approaches are vulnerable against to uncertainties of communication link to different extents. The proposed approach always guarantees the optimal control performance under normal conditions and conditions with link failures, verifying its high robustness. It also has low computation complexity and high optimization efficiency, thus applicable on IoT-enabled BASs.

This paper introduces a kind of open cycle absorption heat wet flue gas heat recovery system, which use CaCl2 as the working medium. The system will use the wet heat recovery method and combined with an efficient heat pump system for flue gas as a heat source generator. Through direct contact with the solution in the absorber, the flue gas is going to carry out gas, liquid heat transfer between heat exchanger, realization of sensible heat and latent heat step by step.As the key part of the system, absorber is established by one-dimensional steady-state heat transfer and mass transfer model. This paper uses the finite difference method to model the discrete numerical methods, and analyzes the characteristics of heat and mass transfer in the absorber. We obtain the concentration curves of the three kinds of working medium's temperature and flow along the height direction. We also analyze the influence of CaCl2 solution parameters changes on the absorption process, parsing the reason of the temperature change by analyzing the three working medium's energy flow trend. We found that the temperature change of flue gas is non-monotonic, which decreases gradually in the range of absorption tower height 0–0.9 m, and then increases gradually. The reason for this change is that sensible heat exchange and latent heat exchange exist between flue gas and solution. Although such a change has an impact on the efficiency of the system, it prevents the ''white smoke'' from condensing in the air, which effectively protects the environment. Compared with conventional LiBr absorption heat pump, the system constructed in this paper has certain advantages in latent heat recovery, flue gas heat energy utilization, energy conservation and emission reduction and economy.

Renewable energy has become an important choice to solve the energy crisis and environmental problems. A sustainable development needs policies and strategies policies, which can improve energy efficiency and reduce greenhouse gas emissions. By collecting the research results released by relevant statistical departments and authoritative institutions, this paper summarizes the international energy development situation, systematically combs the energy development situation of the European Union, United States, Australia, India, Brazil. The development trend of wind, solar, biomass, geothermal, ocean, and hydrogen energy have been analyzed. In addition, this paper proposes that China should draw up its own development path of renewable energy from international development experience, and actively explore renewable energy policies and strategies adapted to different stages of development.

This work presents a cost-effective and environment-friendly form-stabilized phase change material (PCM) and corresponding solar thermal application in the tankless solar water heater (TSWH). Coconut shell charcoal (CSC) as supporting material was modified by moderate oxidant of H2O2 with different concentrations, and then stabilized stearic acid (SA) to prepare composite PCMs through vacuum impregnation. It found that CSC support causes a 15.70% improvement of SA loadage after treated by 15% H2O2 due to coefficient enhancement by physical interaction and surface modification. The modified CSC15 support appears more super macropores which contribute to the impregnation of SA than non-modified CSC0 support verifying from SEM and BET results. And the content of oxygen functional groups was increased after oxidation modification, also motivating SA stabilization by hydrogen bond interaction in XPS analysis. FTIR results proved there is no chemical reaction happened between SA and CSC. Moreover, the latent heat and phase transition temperature of the as-prepared SA/CSC15 composite are 76.69 J g−1 and 52.52 °C, respectively. All composites exhibit excellent thermal stability under a working temperature of 180 °C and form stability during phase change. Thermal energy storage-release test within 70 °C presents the composite has fast heat transfer efficiency than pure SA. The composite filled in TSWH system has 0.75 W m−1 K−1 thermal conductivity which is 2.88 times higher than that of pure SA (0.26 W m−1 K−1). Besides, the TSWH system with a flow rate of 0.004 kg s−1 could heat water effectively after sunset and the energy obtained from the thermal storage system within 1830 s testing times is about 0.15 kW h. In all, SA/CSC composite with good physical-thermo properties has potential in thermal energy storage application, especially in solar energy storage.