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[Display omitted] • A multi-energy complementary heating system (MECHS) suitable for cold rural areas was built. • The MECHS was assessed using the entropy-TOPSIS model, with economic factor contributing more than 50%. • Solar-biomass was the optimal heating mode and had a comprehensive score of 0.6202. The establishment of a multi-energy complementary heating system (MECHS) using abundant renewable energy is currently an effective way to solve the rapid growth of heating demands in rural areas. However, coupling schemes for different energy sources have become a critical barrier to the MECHS development. The entropy-TOPSIS method was used to conduct a comprehensive evaluation of MECHS, which provided a basis for optimizing system operation strategy. We built a test platform for MECHS based on solar-biomass-electricity energy in Tumxuk, Northwest China. Frist, six working modes were established according to the system operation control strategy. Second, we constructed the evaluation indexes of MECHS considering energy, environment and economy and determined their weights using entropy weight method. Third, the TOPSIS model was used to perform a comprehensive evaluation of the different working modes to identify optimal energy supply schemes. Our case study showed that the MECHS operated stably and met the indoor temperature demands of rural houses in cold regions. To maximize the heating efficiency of the system, it is necessary to reduce the types of energy appropriately. Next, based on entropy-TOPSIS model, the recommended order of energy supply mode for heating systems were solar-biomass energy (0.6202), solar-biomass-electric energy (0.6152), solar-electric energy (0.5895), biomass energy (0.4075), biomass-electric energy (0.3680), and electrical energy (0.3603). We found that the optimal energy supply mode was solar-biomass. Our results indicated that the entropy-TOPSIS model could provide practical recommendations for the design and operation strategies of MECHS in cold rural areas. [ABSTRACT FROM AUTHOR]

[Display omitted] • This review compiled indoor PM concentration worldwide that mostly exceeded WHO guidelines. • Indoor PM derived mainly from combustion activities, human movement and outdoor sources. • More attention should be paid to the accumulation mode PM due to highly-penetrating and lowly-depositing. • The indoor/outdoor ratio of PM concentration was primarily affected by human activity and ventilation. • Reasonable ventilation, indoor sources control or using filtration-based air cleaners could reduce indoor PM concentration. Particulate matter (PM) refers to the sum of all solid and liquid particles suspended in the air, many of which are harmful. Exposure to indoor PM with high concentration may cause a series of adverse health effects. This review provides a comprehensive summary of characteristics and influencing factors of indoor PM, including concentration, indoor sources, penetration of outdoor PM, deposition onto indoor surfaces and air exchange rate. Indoor PM pollution is rather serious, and even the indoor concentration in some cities exceeds air quality standards recommended by the World Health Organization. Indoor sources can give rise to a sharp increase in indoor PM concentration, which greatly affects individual exposure and cannot be ignored. Accumulation mode particle is characterized by high penetration and low deposition, which pose a challenge to its control. Ventilation can migrate outdoor PM into indoor environment, especially when ambient PM concentration is high. In order to ensure the health and comfort of indoor built environment, measures should be taken to reduce indoor PM concentrations. Source control is the typical and effective way to control indoor PM. Natural ventilation is the most economically technical means of reducing indoor PM, although uncertainties remain as to its effectiveness for PM control. Filtration is the most well established and widely used technology for internal air cleaning due to its low cost and high efficiency. With the increasing health awareness of people, it is also recommended to clarify the impact of exposure to indoor PM on human health. [ABSTRACT FROM AUTHOR]

A solar air-source heat pump system with phase change energy storage is investigated in this paper. By employing phase change storage in this system, it overcomes the frosting problem in the evaporator and improves the COP of heat pump under the extreme weather condition. The system is constructed and the experiment is carried out in Shijiazhuang. The testing results reveal that the average COP of the heat pump is around 4.92 in the clear days, 1.79 in extreme weather conditions, and 2.12 in the rainy or snow days, which is much higher than the traditional heating mode. Through economic analysis, it is found that the operating cost of the whole heating season is around 22.5% of the oil-fired boiler, 55.4% of the gas boiler, 27.1% of the electric boiler, and 65.6% of the air source heat pump. With the help of phase change storage, solar energy and air source can be utilized to the maximum extent to achieve the purpose of energy-saving and emission reduction in the extremely cold area. [ABSTRACT FROM AUTHOR]

• Propose a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of cooling load and outdoor wet-bulb temperature. • Obtain the optimal match of prediction models and optimization algorithms for improving the performance of the MLB-MPC. • Investigate the impact of disturbances on the performance of global optimization. • Achieve an energy-saving ratio of 7.1% for the air-conditioning system when compared with the measured operation data. The dynamic optimization of key setpoints (e.g., supply water temperatures of chillers and cooling towers, or indoor temperature and humidity) can track the efficient performance point of the air-conditioning system (ACS), thus obtaining a great energy-saving effect. The model predictive control is an effective way to precisely adjust these setpoints. However, most of the existing studies focused on simple linear strategies, e.g., experience-based, rule-based, or component-based, which results in unsatisfactory energy efficiency. Aiming at this, the paper proposed a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of disturbances. The framework first employed the grid search and cross-validation to optimize the total energy consumption prediction models (TECPMs), which were built by data-driven models (e.g., multiple linear regression, artificial neural network, support vector regression (SVR), and random forest). And then the optimization performances of each TECPM to the controlled variables were obtained by using different optimization algorithms (e.g., genetic algorithm, particle swarm optimization (PSO), and simulated annealing). Finally, the optimal match of TECPMs and optimization algorithms was achieved by trade-off among prediction accuracies, optimization accuracies, and optimization time. The case studies demonstrated that the optimal match for the MLB-MPC is SVR and PSO as they had the highest prediction accuracy (mean absolute percentage error of 2.5%) and shortest optimization time (41 ms/per) and optimization accuracies with little difference. After adopting the optimal match, the MLB-MPC with predictive disturbing inputs achieved a great energy-saving ratio of 7.1% for the ACS, which was only less than the MLB-MPC with ideal disturbing inputs by 0.4%. This indicated that exact predictions of disturbances (e.g., cooling load and outdoor wet-bulb temperature) are important for the MLB-MPC. The proposed MLB-MPC framework would provide a method for improving the energy efficiency of ACSs. [ABSTRACT FROM AUTHOR]

Using energy conservation measures (ECMs) for building retrofit can significantly reduce building operational carbon emissions (BOCEs) and contribute to Chinese carbon neutrality by 2060. This paper involves 11 feasible ECMs for commercial buildings in hot-summer and cold-winter zone, China, and proposes a quick assessment approach to BOCEs reduction. Prototypical models are simulated for ECMs using EnergyPlus and the emission intensity is corrected by the building shape factor and window-to-wall ratio. Based on pre-simulated results, effects on reducing emissions of individual measures and packages of them are studied to develop regression models in SPSS. The results show that decreasing the U-factor of external walls, lighting power density (LPD), or shading coefficient can reduce BOCEs linearly. BOCEs are most sensitive to LPD and using daylighting control can reduce emissions by 5–15%. In addition, increasing the boilers' efficiency reduces emissions linearly, while performance improvement of chillers or heat pumps reduces emissions logarithmically. Furthermore, utilizing variable speed pumps, heat recovery ventilation, and air-side economizers can reduce emissions by 3.8%, 6.6%, and 15.2%, respectively. Finally, significant interactions exist among measures of enhancing glazing U-factor, adding external shading, using energy-saving lamps, and improving cooling/heating system efficiencies. The developed regression models are validated in a retrofit case in Shanghai. Through assessment, the case can reduce BOCEs by 15.9 kgCO 2 /m2, with a reduction rate of 20.8%. Compared with recorded data, the relative error of assessing emission reduction is −13.0% and that of assessing reduction rate is 0.5%, which are acceptable in the decision-making stage of retrofit strategies. [ABSTRACT FROM AUTHOR]