32 research outputs found
Addressing uncertainty to achieve stability in urban building energy modeling: A comparative study of four possible approaches
No full textUrban building energy modeling (UBEM) empowers the construction of green and low-carbon cities. However, its development is hindered by the uncertainty of data inputs, including inherent uncertain data (IUD), measurement uncertain data (MUD) and scenario uncertain data (SUD). This paper employed one typical MUD, namely, the thermal parameters of construction assemblies, as the study object to analyze the accuracy and stability of UBEM using four different approaches, i.e., archetypes built with standards, archetypes built with local datasets, probabilistic models and urban factor methods. The results showed that when focusing solely on thermal parameters, the RE values could reach 500 % at the building level but tended to converge to less than 90 % at the district level. In addition, the mean of relative errors at the building level influenced the accuracy at the district level as well as the rate of mean convergence. However, this metric did not affect the threshold to attain range convergence, since its number was fixed, neither related to the sample size nor to the calculation accuracy. This study emphasized that using real data could enhance the accuracy of UBEM, regardless of the archetype or the stochastic approach used, but the distinctions mainly occurred at the building level. Moreover, the large-scale simulation work could be transformed into the task of calculating energy use data for several convergence units, each consisting of dozens of buildings, since these units were able to exhibit stability on their own
Dynamic predictions for the composition and efficiency of heating, ventilation and air conditioning systems in urban building energy modeling
No full textThe composition and efficiency of heating, ventilation and air conditioning (HVAC) systems are crucial inputs for urban building energy modeling (UBEM). However, in current studies, the heterogeneity of HVAC systems is often ignored, leading to huge modeling errors. To address these issues, this study developed a three-step prediction approach that can dynamically forecast the composition of HVAC systems (SystemID), the efficiency of heat sources and the efficiency of entire systems at the urban scale. The performance of the prediction models was evaluated through the ten-fold cross-validation. The results showed that the accuracy in predicting the composition could reach 0.87, and the coefficient of determination (R2) 2 ) was greater than 0.93 in efficiency forecasts. The accuracy of the developed approach was further evaluated through the testing set in two situations, i.e., with the predicted and actual SystemID data. In the first situation, the overall accuracy in predicting the composition reached 78.2 %, and the R2 2 for forecasting the system efficiencies was over 0.6. Then, with the SystemID assumed to be accurate, the R2 2 increased to more than 0.87. To analyze the performance of the developed method in energy use predictions, two case regions in Changzhou and Nanjing were used, respectively. The simulation results were compared with the scenario in which the system efficiencies were determined based on the national standard. The results showed that the developed method could enhance the accuracy of total energy use predictions by approximately 10 % and HVAC energy use predictions by roughly 40 % at both urban and building scales. This helps policy-makers craft energy-saving strategies more reasonably
Genetic algorithm based scheduling model of continuous casting crystallizer copper electroplating
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Does Tax Incentives Matter to Enterprises’ Green Technology Innovation? The Mediating Role on R&D Investment
No full textThis study focused on China’s A-share listed companies from 2017 to 2022, and concluded that tax incentives have a positive impact on the performance of green technology innovation, and that value-added tax preferences are more effective than income tax preferences. Tax incentives guide enterprises to increase R&D investment, and R&D investment constitutes the mechanism of tax incentives to promote the performance green technology innovation. Non-state-owned enterprises benefit more from the timely help of tax incentives. Higher levels of the business environment in certain regions lead to more significant promoting effects of tax incentives. Enterprises in non-heavily polluting industries are more easily incentivized by preferential tax policies to undertake more green innovations. The study’s findings aimed to improve current preferential tax policies and help enterprises achieve green and innovative development
Optimization Design of Capacity and Operation Strategy for Building Level Distributed Energy System
No full textAbstractCombined cooling, heating, and power (CCHP) system is an effective solution to implement energy-step-utilization, and achieve energy conservation and emissions reduction. In this paper, the energy requirements are obtained by energy consumption simulation on a building and are classified to several subcategories by energy grade of exergy. The CCHP systems include natural gas internal combustion engine, heat recovery system, gas boiler, absorption chiller, and heat exchangers. The economic efficiency, energy efficiency, and environmental benefits are taken in account to establish a multi-objective evaluation methodology. According the energy balance principle, an integrated optimization model of CCHP system is established by the means of non-liner programming (NLP). The optimization model will automatic match the energy demands’ grade and the result will show the capacity of all the equipment and the operation strategy in the whole year. The computational results also prove the integrated optimization method is valid
Grazing exclusion-induced shifts, the relative importance of environmental filtering, biotic interactions and dispersal limitation in shaping desert steppe communities, northern China
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