Search

• Propose a generalized paradigm of neighborhood-scale urban building energy modeling. • Determine the typical building shape through field survey and clustering analysis. • Develop massive geometric prototype models for building stock. • Introduce a series of geometric parameters to describe urban morphology. • Identify the most influential urban morphology factors affecting energy demands. The building sector accounts for an increasing amount of energy and emissions with continued urbanization, and improving the energy performance of building stocks at the early planning stage is cost-efficient and easy to operate. This study presents an automatic generation framework to quantify the impacts of urban morphology factors on the energy performance of neighborhood-scale building stocks. The typical office building shape is firstly determined by a field survey and clustering analysis. Then the building modular cells-based method is introduced to establish a comprehensive energy performance database for building stocks. The impacts of urban morphology factors on energy performance are further prioritized via global sensitivity analysis. A case study in Shanghai, China demonstrated the proposed automatic generation framework which can act as a generalized paradigm to model building stocks and be widely applicable to other climate zones. The urban morphology factors representing buildings' geometric characteristics tend to have greater impacts on energy performance, among which the top influencing factors are relative compactness, building coverage ratio, and building height distribution. The proposed framework and the findings are expected to generate valuable insights into energy-efficient urban design. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel composition of PCMW was proposed for complex climates. • Energy-saving potential of PCM-based wallboard was investigated for different locations. • The thermal response of PCM-based wallboard in different climates was analyzed. The incorporation of phase-change material (PCM) into construction materials has been proposed to indirectly accomplish indoor thermal comfort and energy saving. To adapt to seasonal changes (e.g., hot summer and cold winter), a novel type of hybrid PCM-containing wallboard (including three different PCMs) was proposed with a wide phase-change temperature range and that can work in various climates. In this study, a small building equipped with a hybrid PCM wallboard was established to evaluate energy-saving performance using EnergyPlus. To compare different hybrid PCM wallboards, four building models were considered, including Mode 1, Mode 2, Mode 3, and gypsum wallboard (BASE). The building performance in terms of heating and cooling energy consumption over one year was investigated at seven locations in China to represent different climate zones. The results show that less heating and cooling energy was required for Mode 2 than for the other models. Further investigation of the Mode 2 PCM wallboard revealed that the location of the PCM layer also influenced the energy savings. The total energy requirements for Mode 2 were lower than those for BASE by 2.15%, 4.33 %, 1.77 %, 5.56 %, 7.65 %, 5.84 %, and 0.2% for Shanghai, Beijing, Kunming, Lanzhou, Xining, Harbin, and Hong Kong, respectively. We conclude that the hybrid PCM wallboard can effectively reduce the heat gain through the building envelope throughout the year, presenting a considerable advantage of using it in buildings located in Shanghai, which experiences a hot summer and cold winter. [ABSTRACT FROM AUTHOR]

Various algorithms predominantly use data-driven methods for forecasting building electricity consumption. Among them, algorithms that use deep learning methods and, long and short-term memory (LSTM) have shown strong prediction accuracy in numerous fields. However, the LSTM algorithm still has certain limitations, e.g., the accuracy of forecasting the building air conditioning power consumption was not very high. To explore ways of improving the prediction accuracy, this study selects a high-rise office building in Shanghai to predict the air conditioning power consumption and lighting power consumption, respectively and discusses the influence of weather parameters and schedule parameters on the prediction accuracy. The results demonstrate that using the LSTM algorithm to accurately predict the electricity consumption of air conditioners is more challenging than predicting lighting electricity consumption. To improve the prediction accuracy of air conditioning power consumption, two parameters, relative humidity, and scheduling, must be added to the prediction model. [ABSTRACT FROM AUTHOR]

Clothing plays an essential role in maintaining heat balance and thermal comfort for older people but varies with climate conditions and costume culture. The recommended clothing insulation in ISO 7730 and ASHRAE Standard 55 lack information on typical clothing ensemble and local insulation values for older people. To investigate the winter clothing characteristics of older people living in Shanghai, a field survey involving 342 healthy subjects from 12 aged-care homes and a lab study conducted with a 22-segment thermal manikin were performed. Subjects' typical clothing ensembles were summarized based on the survey. The local clothing insulation of 34 typical garments and 35 clothing ensembles were measured in the lab experiment. Results show that most older people in winter worn 7 pieces of clothes (excluding underpants, socks and shoes). The frequencies of typical upper and lower garments/ensembles were reported. Through manikin tests, the whole and local insulation of those typical garments and ensembles were presented. By comparison, the insulation values of garments in ISO 7730 were found to be inconsistent with tested garments' results for older people. Further, linear regression models were built to link relationships of the insulation of multiple garments and ensembles. Through running an older people-based thermoregulation model, it was found an incorrect estimation of typical winter clothing insulation for older people could lead around 1.3 °C deviation on predicted mean skin temperature or around 26% deviation on heat loss. • A field study of 342 elderly people was conducted in 12 aged-care homes in winter. • Local insulation of typical ensembles was tested using a 22-segment thermal manikin. • A winter local clothing insulation database for ederly people was built. • Linear regression models of local clothing insulation were established. • Effects of typical winter clothing for the elderly in predicting skin temperature have been discussed. [ABSTRACT FROM AUTHOR]

The analysis of energy configuration in the planning of data-center-park-integrated energy systems (DCP-IESs) has become an enormous challenge, owing to multi-energy complementarity, energy cascade use, and energy security. In this study, a configuration model of DCP-IESs was established to obtain the economic and low-carbon energy uses of the data centers, based on mixed integer linear programming. In the model, carbon emissions were converted to economic indicators through carbon pricing. Then, the configuration model was modified according to the security of the proposed device switching logic, and the Markov-based reliability estimation method was used to ensure the redundant design of the configuration. Using the new energy configuration method, the DCP-IES configuration scheme could be obtained under economical, low-carbon, and high reliability conditions. A data center park in Shanghai was selected as a case study, and the results are as follows: it will only take 2.88 years for the economics of DCP-IES to reach those of traditional data center energy systems. Additionally, the use of configuration model in DCP-IES would result in a reduction in annual carbon emissions of 39,323 tons, with a power usage effectiveness of 1.388, whereas an increase in reliability results in an increasingly faster increase in the initial investment cost. [ABSTRACT FROM AUTHOR]

Uncertainty complicates the optimization model of distributed energy systems, it is a challenge to address the fragility of optimal solutions, which calls for an effective but convenient approach to introduce uncertainties into multi-objective optimization. This study proposes and compares the priori and the posteriori modeling approaches for optimizing the design of distributed energy systems under uncertainty. The posteriori approach is developed as a Monte Carlo simulation combined with the deterministic programming model, while the priori approach is formulated as a two-stage stochastic programming model. Both approaches consider economic and environmental objectives and use the same set of uncertainty parameters based on a case study in Shanghai. The results show that, compared with the priori-approach model, the posteriori-approach model leads to an underestimation of the total annual cost, but their total annual carbon emission approximates. Besides, the Pareto frontier cliques from the posteriori approach demonstrate the distributions of system performance, whereas the priori approach can capture the uncertainties at the substantially higher computational cost. Finally, the tradeoff between model complexity and computational cost is discussed to generate more insights on the optimal design, i.e., configuration and dispatch, of distributed energy systems. • Develop a multi-objective stochastic optimization model for distributed energy system • Quantify multiple uncertainties throughout the optimum stochastic scenarios • Combine Monte Carlo simulation and multi-objective deterministic optimization • Propose Pareto frontier cliques to exhibit the optimal solution regions • Comparatively analyze the priori and posteriori uncertainty-modeling approaches [ABSTRACT FROM AUTHOR]