• Energy Research

New modelling tools are required to accelerate the decarbonisation of the building sector. Urban building energy modelling (UBEM) has recently emerged as an attractive paradigm for analysing building energy performance at district and urban scales. The balance between the fidelity and accuracy of created UBEMs is known to be the cornerstone of the model’s applicability. This study aimed to analyse the impact of traditionally implicit modeller choices that can greatly affect the overall UBEM performance, namely, (1) the level of detail (LoD) of the buildings’ geometry; (2) thermal zoning; and (3) the surrounding shadowing environment. The analysis was conducted for two urban areas in Stockholm (Sweden) using MUBES—the newly developed UBEM. It is a bottom-up physics-based open-source tool based on Python and EnergyPlus, allowing for calibration and co-simulation. At the building scale, significant impact was detected for all three factors. At the district scale, smaller effects (<2%) were observed for the level of detail and thermal zoning. However, up to 10% difference may be due to the surrounding shadowing environment, so it is recommended that this is considered when using UBEMs even for district scale analyses. Hence, assumptions embedded in UBEMs and the scale of analysis make a difference.

Sustainability transformation of the heating sector is recognised as being essential for reaching climate and environmental targets while improving the quality of life in cities worldwide. Participatory strategic planning enabled by scenario methods can be an important tool to guide this transformation, but methods for qualitative scenario analysis supporting stakeholder participation must be further developed and tested in the context of different cities. This paper presents results from integration of urban energy system modelling into the participatory strategic planning process implemented in the city of Nis, which suffers problems typical of the heating sector in Serbia and the Western Balkans. The aim was to explore how the scenarios developed by local stakeholders could transform the Nis heating system by 2030. Five scenarios developed within participatory backcasting project and a BAU scenario were analysed in terms of decarbonisation, energy security and energy efficiency using Long-range Energy Alternatives Planning System (LEAP). A final scenario “Efficiency for the green future” designed by the stakeholders for implementation in the city included high standards of energy efficiency in buildings, increased share of renewables in the heating energy mix, expanding the district heating system, deploying smart technologies and green architecture. The LEAP model demonstrated that this final scenario could lead to achievement of the desirable future vision developed by stakeholders for Nis, through substantial improvements in energy efficiency and energy security, and to considerable emissions decreases by 2030 in comparison with the base year (2010) and the BAU scenario.

Abstract This study proposes a novel framework, modular participatory backcasting (mPB), for long-term planning in the heating sector. The mPB framework is based on participatory backcasting (PB) and integrates principles of modularity, participatory modelling, and transdisciplinarity. We discerned for mPB 13 modules that can be arranged according to the purpose and specifics of each planning process. The design of the mPB framework and its implementation are presented for the cases of participatory strategic planning processes to achieve sustainable heat provision by 2050 in a Ukrainian city (Bila Tserkva) and a Serbian city (Nis). The results show that mPB allows adaptability to local contexts and limitations through exclusion, augmentation, substitution, splitting and inverting properties of modularity; decreases the learning time for applying the framework in a novel context; increases the reproducibility and transparency of long-term energy planning processes; enables efficient integration of quantitative methods into the participatory process; and advances collaboration between academia and society. The proposed framework is beneficial for advancement of local planning and policy-making practices by creating strategies with a wider support of stakeholders. It could also be useful for further research through cross-case analysis.

We performed systematic mapping of EPC data applications by time, geographical spread, data features & auxiliary data used, problem domains addressed and complexity of employed data analysis. This mapping work was intended to answer the following questions: Q1. Which research studies have used EPC data (hereafter “applications”)? Q2. What input data were used by the EPC data applications? Q3. Which problem domains were addressed by the EPC data applications? Q4. How did the EPC data applications change within the study period? Purpose: To understand how the energy performance certificates (EPC) data has been used since introduction of the first national EPC registers. Kartläggning av tillämpningar av EPC data. En mer detaljerad beskrivning är tillgängligt på den engelska katalogsidan: https://snd.gu.se/en/catalogue/study/SND1066

Abstract This paper presents an approach for using rich datasets to develop different building archetypes depending on the urban energy challenges addressed. Two cases (building retrofitting and electric heating) were analysed using the same city, Stockholm (Sweden), and the same input data, energy performance certificates and heat energy use metering data. The distinctive character of these problems resulted in different modelling workflows and archetypes being developed. The building retrofitting case followed a hybrid approach, integrating statistical and physical perspectives, estimating energy savings for 5532 buildings from seven retrofitting packages. The electric heating case provided an explicitly statistical data-driven view of the problem, estimating potential for improvement of power capacity of the local electric grid at peak electric power of 147 MW. The conclusion was that the growing availability of linked building energy data requires a shift in the urban building energy modelling (UBEM) paradigm from single-logic models to on-request multiple-purpose data intelligence services.