Digital transformation in the built environment offers new opportunities to improve building maintenance through data-driven approaches. Smart monitoring, predictive modeling, and artificial intelligence can enhance decision-making and enable proactive strategies. The preservation of historic buildings is an important scenario where preventive maintenance is essential to ensure long-term sustainability while protecting heritage values. This thesis presents a comprehensive solution for data-driven smart maintenance of historic buildings, integrating Internet of Things (IoT), cloud computing, edge computing, ontology-based data modeling, and machine learning to improve indoor climate management, energy efficiency, and conservation practices.

To enable long-term environmental monitoring, a scalable digitalization solution is developed in Paper I, integrating an IoT-based sensing system with edge and cloud computing. Field deployments confirm the long-run reliability of the system in supporting real-time and historical data analysis for maintenance decisions. Papers II and III further introduce the concept of parametric digital twins, incorporating ontology-based data models to ensure a consistent representation of building structures, systems, and environmental conditions. Case studies at the City Theatre of Norrköping and Löfstad Castle in Östergötland, Sweden, validate the effectiveness of digital twins in identifying indoor climate risks and guiding conservation strategies.

Based on the collected data, Papers IV and VI explore deep learning methods for building energy forecasting. Paper IV evaluates state-of-the-art deep learning architectures for point and probabilistic multi-horizon forecasting, showing that incorporating future exogenous factors improves prediction accuracy. It also highlights how different building operating modes impact forecasting performance. Paper VI integrates deep learning with digital twins to identify energy-saving opportunities and optimize operations.

Papers V and VII focus on predictive modeling for indoor climate management. Paper VII proposes an edge-centric approach as an alternative to cloud-centric solutions, ensuring low latency and data privacy. Paper V explores federated deep learning as a privacy-aware solution for decentralized indoor climate forecasting. A comparative study of federated learning algorithms demonstrates that federated models can achieve prediction accuracy comparable to centralized learning while preserving data privacy. These findings offer practical insights for managing heterogeneous, distributed environmental data to support sustainable building operations.

This thesis advances data-driven conservation of historic buildings by combining smart monitoring, digital twins, and artificial intelligence. The proposed methods enable preventive maintenance and pave the way for the next generation of heritage conservation strategies.

This study showcases the digitalization of Löstad Castle in Sweden to contribute to preserving its heritage values. The castle and its collections are deteriorating due to an inappropriate indoor climate. To address this, thirteen cloud-connected sensor boxes, equipped with 84 sensors, were installed throughout the main building, from the basement to the attic, to continuously monitor various indoor environmental parameters. The collected extensive multi-parametric data form the basis for creating a parametric digital twin of the building. The digital twin and detailed data analytics offer a deeper understanding of indoor climate and guide the adoption of appropriate heating and ventilation strategies. The results revealed the need to address high humidity problems in the basement and on the ground floor, such as installing vapor barriers. Opportunities for adopting energy-efficient heating and ventilation strategies on the upper floors were also highlighted. The digitalization solution and findings are not only applicable to Löfstad Castle but also provide valuable guidance for the conservation of other historic buildings facing similar challenges.

Building energy forecasting facilitates optimizing daily operation scheduling and long-term energy planning. Many studies have demonstrated the potential of data-driven approaches in producing point forecasts of energy use. Despite this, little work has been undertaken to understand uncertainty in energy forecasts. However, many decision-making scenarios require information from a full conditional distribution of forecasts. In addition, recent advances in deep learning have not been fully exploited for building energy forecasting. Motivated by these research gaps, this study contributes in two aspects. First, this study has adapted and applied state-of-the-art deep learning architectures to address the problem of multi-horizon building energy forecasting. Eight different methods, including seven deep learning-based ones, were investigated to develop models to perform both point and probabilistic forecasts. Second, a comprehensive case study was conducted in two public historic buildings with different operating modes, namely the City Museum and the City Theatre, in Norrköping, Sweden. The performance of the developed models was evaluated, and the predictability of different scenarios of energy consumption was studied. The results show that incorporating future information on exogenous factors that determine energy use is critical for making accurate multi-horizon predictions. Furthermore, changes in the operating mode and activities held in a building bring more uncertainty in energy use and deteriorate the prediction accuracy of models. The temporal fusion transformer (TFT) model exhibited strong competitiveness in performing both point and probabilistic forecasts. As assessed by the coefficient of variance of the root mean square error (CV-RMSE), the TFT model outperformed other models in making point forecasts of both types of energy use of the City Museum (CV-RMSE 29.7% for electricity consumption and CV-RMSE 8.7% for heating load). When making probabilistic predictions, the TFT model performed best to capture the central tendency and upper distribution of heating load of the City Museum as well as both types of energy use of the City Theatre. The predictive models developed in this study can be integrated into digital twin models of buildings to discover areas where energy use can be reduced, optimize building operations, and improve overall sustainability and efficiency.

Digital transformation in the built environment generates vast data for developing data-driven models to optimize building operations. This study presents an integrated solution utilizing edge computing, digital twins, and deep learning to enhance the understanding of climate in buildings. Parametric digital twins, created using an ontology, ensure consistent data representation across diverse service systems equipped by different buildings. Based on created digital twins and collected data, deep learning methods are employed to develop predictive models for identifying patterns in indoor climate and providing insights. Both the parametric digital twin and deep learning models are deployed on edge for low latency and privacy compliance. As a demonstration, a case study was conducted in a historic building in Östergötland, Sweden, to compare the performance of five deep learning architectures. The results indicate that the timeseries dense encoder model exhibited strong competitiveness in performing multi-horizon forecasts of indoor temperature and relative humidity with low computational costs.

Smart maintenance of historic buildings involves integration of digital technologies and data analysis methods to help maintain functionalities of these buildings and preserve their heritage values. However, the maintenance of historic buildings is a long-term process. During the process, the digital transformation requires overcoming various challenges, such as stable and scalable storage and computing resources, a consistent format for organizing and representing building data, and a flexible design to integrate data analytics to deliver applications.

This licentiate thesis aims to address these challenges by proposing a digitalization framework that integrates Internet of Things (IoT), cloud computing, ontology, and machine learning. IoT devices enable data collection from historic buildings to reveal their latest status. Using a public cloud platform brings stable and scalable resources for storing data, performing analytics, and deploying applications. Ontologies provide a clear and concise way to organize and represent building data, which makes it easier to understand the relationships between different building components and systems. Combined with IoT devices and ontologies, parametric digital twins can be created to evolve with their physical counterparts. Furthermore, with machine learning, digital twins can identify patterns from data and provide decision-makers with insights to achieve smart maintenance.

Papers I-III have shown that data can be reliably collected, transmitted, and stored in the cloud. Results of Paper IV indicate that a digital twin that depicts the latest status of a historic building can be created and fed with real-time sensor data. The insights discovered from the digital twin provide facts for improving the indoor climate to achieve both heritage conservation and human comfort. Papers V and VI have shown that deep learning methods exhibit strong capabilities in capturing tendency and uncertainty in building energy consumption. Incorporating future information that determines energy consumption is critical for making multi-horizon predictions. Moreover, changes in the operating mode of a building and activities held in a building bring more uncertainty in energy consumption and deteriorate the performance of point forecasts. 

Overall, this thesis contributes to the field of preservation of historic buildings by proposing a comprehensive digitalization framework that integrates various advanced digital technologies to provide a holistic approach to achieve smart maintenance of historic buildings.

Smart underhåll av kulturhistoriska byggnader med digital teknologi och dataanalys underlättar bevarandet av det kulturhistoriska värdet såväl som anpassning för olika användning. Lokalt utplacerade uppkopplade sakernas internet enheter (Internet of Things, IoT) möjliggör realtidsövervakning av miljösensordata. Genom att analysera insamlade data så kan beslutsfattare identifiera och proaktivt hantera potentiella risker i byggnaden. Underhåll av kulturhistoriska byggnader är ett långsiktigt arbete där varje åtgärd kan få långtgående konsekvenser. Digitala verktyg kan därför bidra dels genom bättre historisk spårbarhet, dels genom bättre prediktion av vad som kommer att hända med byggnaden. En lyckad digital transformering kräver stabila och skalbara lagrings- och beräkningsresurser för att organisera och presentera byggnadsdata. Flexibla applikationer med väl integrerad dataanalys är viktigt för att teknologins fulla potential ska kunna nås.

Denna licentiatavhandling presenterar ett digitaliseringsramverk som adresserar dessa utmaningar genom att integrera IoT, molnberäkning, ontologisk modellering och maskininlärning. IoT-enheterna möjliggör realtidsövervakning av byggnadens status. Användningar en publika molnplattform erbjuder stabila och skalbara resurser för att lagra och analysera data. Ontologi ger ett klart och koncist sätt att organisera och representera byggnadsdata, vilket gör det enklare att förstå hur olika ingående delar påverkar byggnaden. Från detta kan fysikaliskt motsvarande digitala tvillingar skapas. Genom att applicera maskininlärning på dessa tvillingar så kan mönster identifieras som ger beslutsfattaren all nödvändig information för ett smart, väl optimerat underhåll av byggnaden.

Artikel I och II fokuserar på konceptformulering och validering av principen. Artikel I går igenom metoden som används för att skapa digitala tvillingar av historiska byggnader. Artikel II presenterar en referensimplementation av metoden. Den implementerade lösningen är ett komplett system för datainsamling, dataöverföring genom en edge-plattform och datalagring med Microsoft Azure Cloud. Artikel III presenterar fälttest med det egenutvecklade systemet i tre olika historiska byggnader, nämligen Stadsteatern, Stadsmuseet och Hörsalen i Norrköping, Sverige. Fälttestet verifierar stabiliteten hos systemet när det gäller långsiktig drift för datainsamling. Artikel IV introducerar ontologisk modellering till systemet för att tillhandahålla ett enhetligt format för att organisera och representera byggnadsdata. En fallstudie utfördes i Stadsteatern för att verifiera lösningens användbarhet, det studerades hur antalet besökare påverkar inomhusklimatet och potentiella risker identifierades. Artikel V och VI jämför prestanda hos moderna djupinlärningsmetoder med avseende på förmåga att prognostisera byggnaders energiförbrukning. Artikel V fokuserar på prestanda hos egenutvecklade prediktiva modeller vilka utvärderades i Stadsteatern och Stadsmuseet, som utgör två olika driftsfall. Artikel VI visar vad kombinationen av prediktiva modeller och digitala tvillingar kan göra för att förbättra byggnaders energiprestanda.

Sammanfattningsvis bidrar denna avhandling till bevarande av kulturhistoriskt viktiga byggnader med ett omfattande digitaliseringsramverk. Ett ramverk som integrerar olika digitala teknologier med en holistisk strategi för att möjliggöra smart underhåll av historiska byggnader.

Digital transformation in buildings accumulates massive operational data, which calls for smart solutions to utilize these data to improve energy performance. This study has proposed a solution, namely Deep Energy Twin, for integrating deep learning and digital twins to better understand building energy use and identify the potential for improving energy efficiency. Ontology was adopted to create parametric digital twins to provide consistency of data format across different systems in a building. Based on created digital twins and collected data, deep learning methods were used for performing data analytics to identify patterns and provide insights for energy optimization. As a demonstration, a case study was conducted in a public historic building in Norrköping, Sweden, to compare the performance of state-of-the-art deep learning architectures in building energy forecasting.

Historic buildings require good maintenance to sustain their function and preserve embodied heritage values. Previous studies have demonstrated the benefits of digitalization techniques in improving maintenance and managing threats to historic buildings. However, there still lacks a solution that can consistently organize data collected from historic buildings to reveal operating conditions of historic buildings in real-time and to facilitate various data analytics and simulations. This study aims to provide such a solution to help achieve preventive conservation. The proposed solution integrates Internet of Things and ontology to create digital twins of historic buildings. Internet of Things enables revealing the latest status of historic buildings, while ontology provides a consistent data schema for representing historic buildings. This study also gives a reference implementation by using public cloud services and open-source software libraries, which make it easier to be reused in other historic buildings. To verify the feasibility of the solution, we conducted a case study in the City Theatre, Norrköping, Sweden. The obtained results demonstrate the advantages of digital twins in providing maintenance knowledge and identifying potential risks caused by fluctuations of relative humidity.

Information and communication technologies (ICTs) help preserve historic buildings and optimize energy efficiency. This study proposes a digitalization framework for historic buildings by utilizing ICTs, such as Internet of Things (IoT), digital twins, and cloud computing. A digital twin is a digital representation of physical world assets that genuinely reflects the properties of real-world objects and processes. In this study, historic buildings are modeled with cloud-based digital twins. Indoor environmental data are collected with locally deployed sensors and ingested to a digital twin in real-time. The digital twin enables decision-makers to remotely monitor the indoor environment of a historic building and actively manipulate actuators to perform maintenance. Empowered by data analytics and artificial intelligence (AI), a digital twin can further simulate and predict state changes in a historic building to reach desired autonomous maintenance and energy saving.

Researches on the Internet of Things (IoT) and cloud computing have been pervasive in both the academic and industrial world. IoT and cloud computing are seen as cornerstones to digital transformation in the industry. However, restricted by limited resources and the lack of expertise in information and communication technologies, small- and medium-sized enterprises (SMEs) have difficulty in achieving digitalization of their business. In this paper, we propose a reference framework for SMEs to follow as a guideline in the journey of digital transformation. The framework features a three-stage procedure that covers business, technology, and innovation, which can be iterated to drive product and business development. A case study about digital transformation taking place in the vertical plant wall industry is detailed. Furthermore, some solution design principles that are concluded from real industrial practice are presented. This paper reviews the digital transformation practice in the vertical plant wall industry and aims to accelerate the pace of SMEs in the journey of digital transformation.