Housing cooperative is a common type of multifamily building ownership in Sweden. Although this ownership structure grants decision-making autonomy, it places a burden of responsibility on cooperative's board members. Most board members lack the resources or expertise to manage properties and their energy consumption. This ignorance presents a unique challenge, especially given the EU directives that prohibit buildings rated as energy classes F and G by 2033. Conversational agents (CAs) enable human-like interactions with computer systems, facilitating human-computer interaction across various domains. In our case, CAs can be implemented to support cooperative members in making informed energy retrofitting and usage decisions. This paper introduces a Conversational agent system, called SPARA, designed to advise cooperatives on energy efficiency. SPARA functions as an energy efficiency advisor by leveraging the Retrieval-Augmented Generation (RAG) framework with a Language Model(LM). The LM generates targeted recommendations based on a knowledge base composed of email communications between professional energy advisors and cooperatives' representatives in Stockholm. The preliminary results indicate that SPARA can provide energy efficiency advice with precision 80%, comparable to that of municipal energy efficiency (EE) experts. A pilot implementation is currently underway, where municipal EE experts are evaluating SPARA performance based on questions posed to EE experts by BRF members. Our findings suggest that LMs can significantly improve outreach by supporting stakeholders in their energy transition. For future work, more research is needed to evaluate this technology, particularly limitations to the stability and trustworthiness of its energy efficiency advice.

Energy retrofitting of existing buildings is an effective strategy for mitigating climate change, offering co-benefits such as improved quality of life, health, and economic growth. However, scaling retrofitting efforts to meet climate targets remains challenging, as it traditionally requires costly expertise to identify high-potential buildings and optimal energy conservation measures (ECMs). Urban Building Energy Modelling (UBEM) has emerged as a powerful tool for large-scale assessments, evaluating ECM effects across districts and cities. Yet, effectively applying retrofitting UBEM (RUBEM) requires careful attention to model complexity, data availability, and the context-specific purpose of modelling. Current automated tools, while helpful, require extensive data and simulations, and still yield estimates with high uncertainty, highlighting the need for new scalable models. While various RUBEMs have been reported in the literature, a thorough analysis is needed to identify challenges and the effective strategies to address them. This study aims to set the research agenda for RUBEM. It presents a review of existing RUBEMs, looking into their core objectives, capabilities, and performance to define functional requirements, challenges, and modelling. The review has shown that typical model applications include matching ECMs to buildings, estimation of ECM effects on individual and system levels, and optimisation of renovation strategies across large portfolios with respect to economic, environmental, energy, cultural, and construction constraints. Data scarcity and quality, model accuracy and validity, model complexity, and computational burden were identified as challenges. These lead to trade-offs between accuracy, speed, and feasibility, which should be driven by the purpose and scale of each RUBEM.

Urban Building Energy modelling (UBEM) has emerged in the last decade as an important tool to accelerate energy transition in the building sector. To fulfil one of its major purposes - forecasting energy savings for potential energy conservation measures at an urban scale, several challenges are still to be addressed. Two key challenges include: 1) the need to calibrate models with a large number of unknown parameters across numerous buildings (either individually or through archetype representation); 2) the limited availability of high-resolution measured data, which raises concerns about calibrated models based solely on yearly values for accurate energy savings forecast. This study addresses these challenges using a case study of 35 buildings in a district in Stockholm, Sweden. Firstly, a new iterative Approximate Bayesian Computation (ABC) method for calibration is proposed, incorporating a copula-based sampling process at each iteration. Secondly, the new calibration process is applied with three different time resolutions to examine the impact of data resolution on forecasted energy usage, particularly when applied to a classical energy conservation measure. Results demonstrate the efficiency of the new method across the 35 buildings, facilitating the rapid population of a final joint distribution for the nine unknown parameters considered in each building. While the marginals may be strongly influenced by the time resolution, the forecasted energy consumption remains identical across the three analysed time resolutions. However, a noticeable difference is observed when the ECM pertains to a formerly unknown or known parameter.

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.

Decarbonisation of the building stock is essential for energy transitions towards climate-neutral cities in Sweden, Europe and globally. Meeting 1.5°C scenarios is only possible through collaborative efforts by all relevant stakeholders — building owners, housing associations, energy installation companies, city authorities, energy utilities and, ultimately, citizens. These stakeholders are driven by different interests and goals. Many win-win solutions are not implemented due to lack of information, transparency and trust about current building energy performance and available interventions, ranging from city-wide policies to single building energy service contracts. The emergence of big data in the building and energy sectors allows this challenge to be addressed through new types of analytical services based on enriched data, urban energy models, machine learning algorithms and interactive visualisations as important enablers for decision-makers on different levels.

The overall aim of this thesis was to advance urban analytics in the building energy domain. Specific objectives were to: (1) develop and demonstrate an urban building energy modelling framework for strategic planning of large-scale building energy retrofitting; (2) investigate the interconnection between quality and applications of urban building energy data; and (3) explore how urban analytics can be integrated into decision-making for energy transitions in cities. Objectives 1 and 2 were pursued within a single case study based on continuous collaboration with local stakeholders in the city of Stockholm, Sweden. Objective 3 was addressed within a multiple case study on participatory modelling for strategic energy planning in two cities, Niš, Serbia, and Stockholm. A transdisciplinary research strategy was applied throughout.

A new urban building energy modelling framework was developed and demonstrated for the case of Stockholm. This framework utilises high-resolution building energy data to identify buildings and retrofitting measures with the highest potential, assess the change in total energy demand from large-scale retrofitting and explore its impact on the supply side. Growing use of energy performance certificate (EPC) data and increasing requirements on data quality were identified in a systematic mapping of EPC applications combined with assessment of EPC data quality for Stockholm. Continuity of data collaborations and interactivity of new analytical tools were identified as important factors for better integration of urban analytics into decision-making on energy transitions in cities.

Energiomställningen till ett fossilfritt byggnadsbestånd är avgörande för att uppnå klimatneutrala städer i Sverige, Europa och övriga världen. Alla scenarier som begränsar uppvärmningen till 1,5 °C är beroende av samarbete mellan alla relevanta aktörer — fastighetsägare, bostadsrättsföreningar, byggföretag, energiföretag och i slutändan även medborgarna. Dessa intressenter drivs av olika intressen och mål. Många vinna-vinna-lösningar implementeras inte på grund av brist på information, transparens och tillit gällande byggnaders energiprestanda. Detta leder till att tillgängliga åtgärder, från enskilda byggnader till policyer för hela städer, inte genomförs. Framväxten av big data inom fastighet- och energisektorn öppnar nya möjligheter att hantera denna utmaning. En nyckel i detta är analytiska tjänster baserade på strukturerad data, urbana energimodeller, maskininlärning och interaktiv visualisering som möjliggörare för beslutsfattande på olika nivåer.

Det övergripande syftet med denna avhandling var att vidareutveckla urban energianalys (eng. urban analytics) inom byggnadsbeståndet. Specifika mål var att: (1) utveckla och demonstrera ett ramverk inriktat mot urbana energimodeller för strategisk planering av storskalig energieffektivisering av byggnader; (2) utreda relationen mellan datakvalitet och tillämpningar av urban energidata för byggnader; och (3) utforska hur urban analys kan integreras i beslutsfattande för energiomställning av städer. Mål 1 och 2 uppnåddes genom en enskild fallstudie baserad på kontinuerligt samarbete med lokala intressenter i Stockholms kommun. Mål 3 behandlades inom en multipel fallstudie som var inriktad på deltagande modellering (eng. participatory modelling) för strategisk energiplanering i två städer, Niš i Serbien och Stockholm. En tvärvetenskaplig forskningsstrategi tillämpades inom hela forskningsstudien.

Ett nytt ramverk inom modellering av urban energi utvecklades och demonstrerades för fallstudien i Stockholm. Detta ramverk använde högupplöst byggnadsenergidata för att identifiera de byggnader och renoveringsåtgärder som har störst potential, undersöka förändringen av det totala energibehovet utifrån storskalig renovering och utreda dess inverkan på energisystemet och tillförseln. Ökad användning av data från energideklarationer (eng. EPC, energy performance certificate) och högre krav på datakvalitet identifierades i en systematisk kartläggning av EPC-tillämpningar, där även en kvalitetsgranskning av energideklarationer i Stockholm kommun genomfördes. Långsiktig datasamverkan och ökad interaktivitet i de nya analytiska verktygen identifierades som viktiga faktorer för bättre integration av urbana energimodeller i beslutsfattande gällande energiomställningar i städer.

Декарбонізація будівель є необхідною умовою енергетичного переходу до кліматично нейтральних міст у Швеції, Європі та у всьому світі. Забезпечення 1.5 °C сценаріїв зміни клімату є можливим лише в разі спільних зусиль усіх зацікавлених сторін — власників будівель, енергетичних компаній, міської влади, енергетичних підприємств та, врешті-решт, громадян. Вищеназвані зацікавлені сторони у своїй діяльності керуються різними інтересами та цілями. Велика кількість потенційно безпрограшних рішень не впроваджується через відсутність необхідної інформації, нестачу прозорості та довіри до інформації про поточну енергоефективність будівель та можливі дії щодо її покращення. До таких заходів можуть відноситися як законодавчі ініціативи на рівні міст, так й енергосервісні контракти для окремих будівель. Нещодавна поява великих даних у житловому та енергетичному секторах дає можливість вирішувати ці проблеми за допомогою аналітики нового рівня. Ця аналітика має бути заснована на розширених даних (англ. enriched data), моделях енергетичних систем міст, алгоритмах машинного навчання та інтерактивних візуалізаціях, що дають змогу істотно підвищити ефективність прийняття рішень на різних рівнях.

Метою цього дослідження було вдосконалення міської аналітики для енергетики будівель (англ. urban analytics for building energy). Це передбачало вирішення наступних задач: (1) розробити та продемонструвати методологію для моделювання енергосистеми міських будівель з метою стратегічного планування масштабного покращення енергоефективності будівель; (2) дослідити взаємозв’язок між якістю та ефективністю використання даних про стан енергетики міських будівель; та (3) дослідити можливості інтеграції аналітики міст в процес прийняття рішень задля енергетичного переходу в містах. Задачі 1 та 2 було реалізовано в рамках окремого кейсу, на основі  постійної співпраці з зацікавленими сторонами в місті Стокгольм (Швеція). Задачу 3 було розглянуто на базі двох кейсів моделювання з участю зацікавлених сторін (англ. participatory modelling) з метою стратегічного енергетичного планування в двох містах — Ніші (Сербія) та Стокгольмі. В обох кейсах застосовувалася стратегія трансдисциплінарного дослідження.

В результаті дослідження було розроблено нову методологію для моделювання енергосистеми міських будівель, використання якої було продемонстровано на прикладі Стокгольма. Ця методологія використовує дані енергоспоживання будівель високої роздільної здатності для (а) виявлення будівель та заходів щодо покращення їхньої енергоефективності з найбільшим потенціалом; (б) оцінювання зміни загального енергоспоживання внаслідок масштабної модернізації будівель та (в) аналізу впливу цих змін на енергопостачання. Було здійснено картування випадків використання даних сертифікатів енергетичної ефективності будівель (англ. energy performance certificates, EPC) та оцінювання якості цих даних для м. Стокгольм. Це дало змогу виявити збільшення обсягів використання даних EPC та зростання вимог до їх якості. Аналіз двох кейсів стратегічного планування в містах продемонстрував, що тривала співпраця щодо збору та використання даних та інтерактивність нових інструментів з аналізу є важливими факторами покращення інтеграції міської аналітики в процеси прийняття рішень задля енергетичних переходів у містах.

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.

Limiting global warming to 1.5 degrees C requires a substantial decrease in the average carbon intensity of buildings, which implies a need for decision-support systems to enable large-scale energy efficiency improvements in existing building stock. This paper presents a novel data-driven approach to strategic planning of building energy retrofitting. The approach is based on the urban building energy model (UBEM), using data about actual building heat energy consumption, energy performance certificates and reference databases. Aggregated projections of the energy performance of each building are used for holistic city-level analysis of retrofitting strategies considering multiple objectives, such as energy saving, emissions reduction and required social investment. The approach is illustrated by the case of Stockholm, where three retrofitting packages (heat recovery ventilation; energy-efficient windows; and a combination of these) were considered for multi-family residential buildings constructed 1946-1975. This identified potential for decreasing heat demand by 334 GWh (18%) and consequent emissions reduction by 19.6 kt-CO2 per year. The proposed method allows the change in total energy demand from large-scale retrofitting to be assessed and explores its impact on the supply side. It thus enables more precisely targeted and better coordinated energy efficiency programmes. The case of Stockholm demonstrates the potential of rich urban energy datasets and data science techniques for better decision making and strategic planning.

Energy performance certificates (EPC) were introduced in European Union to support reaching energy efficiency targets by informing actors in the building sector about energy efficiency in buildings. While EPC have become a core source of information about building energy, the domains of its applications have not been studied systematically. This partly explains the limitation of conventional EPC data quality studies that fail to expose the essential problems and secure effective use of the data. This study reviews existing applications of EPC data and proposes a new method for assessing the quality of EPCs using data analytics. Thirteen application domains were identified from systematic mapping of 79 papers, revealing increases in the number and complexity of studies and advances in applied data analysis techniques. The proposed data quality assurance method based on six validation levels was tested using four samples of EPC dataset for the case of Sweden. The analysis showed that EPC data can be improved through adding or revising the EPC features and assuring interoperability of EPC datasets. In conclusion, EPC data have wider applications than initially intended by the EPC policy instrument, placing stronger requirements on the quality and content of the data.

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.