Search

• A novel vector field-based SVR for building energy consumption prediction (ECP) is proposed. • Nonlinearity of the ECP models is transformed into linearity via vector field-based SVR. • Two high precision, generalization capability, and robustness energy consumption prediction models are established. • A real case study demonstrates the superiority of this study compared with previous studies. Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Data-driven approaches, such as artificial neural networks, support vector regression, gradient boosting regression and extreme learning machine are the most advanced methods for building energy prediction. However, owing to the high nonlinearity between inputs and outputs of building energy consumption prediction models, the aforementioned approaches require improvement with regard to the prediction accuracy, robustness, and generalization ability. To counter these shortcomings, a novel vector field-based support vector regression method is proposed in this paper. Through multi-distortions in the sample data space or high-dimensional feature space mapped by a vector field, the optimal feature space is found, in which the high nonlinearity between inputs and outputs is approximated by linearity. Hence, the proposed method ensures a high accuracy, a generalization ability, and robustness for building energy consumption prediction. A large office building in a coastal town of China is used for a case study, and its summer hourly cooling load data are used as energy consumption data. The results indicate that the proposed method achieves better performance than commonly used methods with regard to the accuracy, robustness, and generalization ability. [ABSTRACT FROM AUTHOR]

Low-concentration PV (CPV, concentrating photovoltaic) technology is a promising concept because it can work with the fixed installation. However, besides the economic consideration, the environmental impacts of the CPV module throughout its life cycle should be addressed as compared with the flat PV technology. Thus, in this paper, a novel high optical performance low-concentration concentrator namely asymmetric compound parabolic concentrator (aCPC) for building south wall integration is proposed. And based on the proposed aCPC-PV module, a life cycle assessment (LCA) has been performed for the low-concentration PV in China to make a scientific comparison with the PV module with the same output level environmentally. Several environmental indicators are calculated for Beijing, Hefei, Lhasa, Lanzhou, Harbin. The primary energy demand, energy payback time and environmental impacts are considered over the entire life cycle of the aCPC-PV module. The results show that the primary energy demand, energy payback time and environmental impacts of the aCPC-PV module are all relatively lower than that of the PV module with the same output. It is confirmed by the LCA study that the aCPC-PV module on behalf of the low-concentration PV technology is still a feasible and effective way for actual engineering because it’s more economic and more environmental friendly than the PV technology although the PV is experiencing continuous decrease in price and increase in efficiency. [ABSTRACT FROM AUTHOR]

• BIM-DB can effectively simulate building performance parameters. • The PSO-RF-NSGA-III hybrid method can be used to predict and optimize building performance. • NSGA can simultaneously optimize energy consumption, carbon emissions and thermal comfort. • The optimal building parameters are different in different regions. Near-zero-energy-consumption buildings (NZEBs) are of great significance for sustainable development, and their design and research have attracted increasing academic attention. To drive and realize the energy saving design and achieve carbon emission and thermal comfort optimization of NZEBs, in this paper, an intelligent optimization method integrating the BIM-DB and PSO-RF-NSGA-III method is established. Multiobjective optimization problems involving NZEBs in four typical climate regions in China are explored. With a typical office building as an example, first, simulation calculations regarding the energy consumption, carbon emissions and indoor thermal comfort in the four climatic regions are performed based on orthogonal tests and BIM-DB. Second, the nonlinear mapping relationships between building design parameters and prediction targets are constructed with the PSO-RF model, which is trained with sample data. The obtained nonlinear mapping relations are used to establish the objective function of NSGA-III, and the multiobjective Pareto-optimal solution set is obtained with the developed PSO-RF-NSGA-III algorithm. Finally, the only optimal solution is determined by using the ideal point method, and reference near-zero-energy-consumption office building parameters are calculated for different climate regions. The conclusions are as follows. (1) The PSO-RF algorithm can efficiently predict building energy consumption, carbon emissions and thermal comfort. In the four regions, the goodness of fit of the three targets is greater than 0.94. (2) Multiobjective optimization can be performed with the proposed RF-NSGA-III intelligent optimization method. After optimizing multiple groups of optimization schemes and adopting energy saving measures, the energy consumption levels in the four climate regions are reduced by 39.72 %, 32.22 %, 26.94 % and 35.37 %, and the other goals are optimized. (3) Index calculations indicate that the optimized building design parameters meet the specified standards for NZEBs, and the main influencing factors and corresponding measures vary from region to region. [ABSTRACT FROM AUTHOR]

Solar energy for building applications may significantly reduce the conventional energy consumption and the related carbon dioxide emissions. The comprehensive utilization of integrated solar thermal and photovoltaic systems is undoubtedly a subject of interest. In the present paper, an optimization model was proposed for integrated solar energy systems, aiming to figure out the optimal utilization and economical efficiency of solar energy resources for buildings in cold plateau areas. A case study in Lhasa city was further carried out in order to evaluate the energy and economic performance of the developed model. The results indicated that solar photovoltaic systems are preferred than solar thermal systems for typical office buildings in cold plateau areas with rich solar energy resources. In addition, a sensitivity analysis was performed to investigate the influences of financial subsidies and commercial electricity prices on the system economical performance. Furthermore, life cycle assessment was conducted to compare and analyze the performances of an optimization system and a conventional system. [ABSTRACT FROM AUTHOR]

Over the recent decades, China’s building energy consumption has been growing rapidly. Also the fuel mix has been changing quickly with more natural gas & electricity and less coal being used. Meanwhile, the technologies used in building sector are improving in efficiency. In this paper, the impact of technical progress and the use of renewable energy in building sector are analyzed. The energy saving and emission reduction potential of building sector are also measured. China TIMES model is used to model the future energy consumption and carbon emissions in building sector. The modelling results indicate that building energy consumption is expected to grow to around 41.6EJ in the reference scenario in 2050. The energy saving potential in 2050 can be up to 4EJ due to the improvement of both building insulation (envelop) and energy use technologies. Renewable energy used in buildings can be a great contributor to the carbon mitigation in buildings. China’s building sector can reach a relatively low-carbon future with more low- and non-carbon fuels consumed. [ABSTRACT FROM AUTHOR]

Abstract: Current energy designs for a building in China focus on the energy efficiency of the whole building and thus often neglect the energy difference between different housing units in apartment buildings. The best design technique in terms of energy savings is not only to decrease whole building energy demand, but also to reduce the energy difference between different housing units to a relatively low level. This paper introduces an important index “energy performance difference between housing units” (EDH) to evaluate the drawbacks of conventional designs on a typical residential building in hot summer and cold winter zone. Then nine improved design options were considered as the possible strategies to diminish the EDH, based on a number of building simulations with the program DeST-h. Results show that the option 9 (add movable shading for the east facing windows and west facing windows, and reduce the U-values of the east walls and west facing windows), an improved design strategy according to the EDH of the current design, reduces the EDH to less than 4% that is much lower than other options and meanwhile ensures a decrease in whole building energy demands. A case study was carried out to validate the effectiveness of this index in optimization. As a conclusion, EDH is a very useful index for optimizing energy designs of apartment buildings and can be used in China to improve conventional building designs. [Copyright &y& Elsevier]