Installing photovoltaic (PV) systems is an essential step for low-carbon development. The economics of PV systems are strongly impacted by the electricity price and the shadowing effect from neighboring buildings. This study evaluates the PV generation potential and economics of 20 cities in China under three shadowing conditions. First, the building geometry models under three shadowing conditions for the 20 cities were constructed using QGIS. Then, 60 building models with PV systems and shadows from surrounding buildings were generated by City Buildings, Energy, and Sustainability (CityBES), an open platform, to simulate the PV power generation. Finally, the study presented one economic analysis model to evaluate the profitability by combining the market cost of rooftop PV systems and electricity prices in China. The economic model included four indicators: payback period (static and dynamic), net present value (NPV), and internal rate of return (IRR). The results show that the reduction of PV power generation ranges from 8.29% to 16.01% under medium shadowing, and experiences a maximum decrease of up to 39.71% under high shadowing. Further economic analysis shows that almost all the regions show reliable potential, obtaining an IRR higher than the reference value (5%). Nenjiang has the highest economic profit, with the highest NPV (86,181.15 RMB) and IRR (30.14%) under no shadowing among 20 cities. It also should be mentioned that the alignment between electricity price distribution and the solar power generation curve will directly impact the economic potential of PV systems.

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization. Urban building energy modeling (UBEM) is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations, supporting the implementation of carbon emission reduction policies. Currently, most studies focus on the energy performance of archetype buildings under climate change, which is hard to obtain refined results for individual buildings when scaling up to an urban area. Therefore, this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas, by taking two urban neighborhoods comprising 483 buildings in Geneva, Switzerland as case studies. In this regard, GIS datasets and Swiss building norms were collected to develop an archetype library. The building heating energy consumption was calculated by the UBEM tool—AutoBPS, which was then calibrated against annual metered data. A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%. The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results showed a decrease of 22%–31% and 21%–29% for heating energy consumption, an increase of 113%–173% and 95%–144% for cooling energy consumption in the two neighborhoods by 2050. The average annual heating intensity dropped from 81 kWh/m² in the current typical climate to 57 kWh/m² in the SSP5-8.5, while the cooling intensity rose from 12 kWh/m² to 32 kWh/m². The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7% and 18.6%, respectively, in the SSP scenarios. The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

This study developed a rapid building modeling tool, AutoBPS-BIM, to transfer the building information model (BIM) to the building energy model (BEM) for load calculation and chiller design optimization. An eight-storey office building in Beijing, 33.2 m high, 67.2 m long and 50.4 m wide, was selected as a case study building. First, a module was developed to transfer BIM in IFC format into BEM in EnergyPlus. Variable air volume systems were selected for the air system, while water-cooled chillers and boilers were used for the central plant. The EnergyPlus model calculated the heating and cooling loads for each space as well as the energy consumption of the central plant. Moreover, a chiller optimization module was developed to select the optimal chiller design for minimizing energy consumption while maintaining thermal comfort. Fifteen available chillers were included, with capacities ranging from 471 kW to 1329 kW. The results showed that the cooling loads of the spaces ranged from 33 to 100 W/m² with a median of 45 W/m², and the heating load ranged from 37 to 70 W/m² with a median of 52 W/m². The central plant’s total cooling load under variable air volume systems was 1400 kW. Compared with the static load calculation method, the dynamic method reduced 33% of the chiller design capacity. When two chillers were used, different chiller combinations’ annual cooling energy consumption ranged from 10.41 to 11.88, averaging 11.12 kWh/m². The lowest energy consumption was 10.41 kWh/m² when two chillers with 538 kW and 1076 kW each were selected. Selecting the proper chiller number with different capacities was critical to achieving lower energy consumption, which achieved 12.6% cooling system energy consumption reduction for the case study building. This study demonstrated that AutoBPS-BIM has a large potential in modeling BEM and optimizing chiller design.

Urban building energy modeling has become an efficient way to understand urban building energy use and explore energy conservation and emission reduction potential. This paper introduced a method to identify archetype buildings and generate urban building energy models for city-scale buildings where public building information was unavailable. A case study was conducted for 68, 966 buildings in Changsha city, China. First, clustering and random forest methods were used to determine the building type of each building footprint based on different GIS datasets. Then, the convolutional neural network was employed to infer the year built of commercial buildings based on historical satellite images from multiple years. The year built of residential buildings was collected from the housing website. Moreover, twenty-two building types and three vintages were selected as archetype buildings to represent 59, 332 buildings, covering 87.4% of the total floor area. Ruby scripts leveraging on OpenStudio-Standards were developed to generate building energy models for the archetype buildings. Finally, monthly and annual electricity and natural gas energy use were simulated for the blocks and the entire city by EnergyPlus. The total electricity and natural gas use for the 59, 332 buildings was 13, 864 GWh and 23.6 × 10⁶ GJ. Three energy conservation measures were evaluated to demonstrate urban energy saving potential. The proposed methods can be easily applied to other cities in China.