Browsing by Author "Ihasalo, Heikki, Prof., Aalto University, Finland"

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  • Improving perceived indoor conditions using building information models and field data
    (2020) Halmetoja, Esa
     School of Engineering | Doctoral dissertation (article-based)
    Facility management (FM) is known as a rapidly developing business sector. Digitalisation appeared and big data emerged during the last twenty years, but however, the essential processes of FM are still poorly digitalised. Same concerns its sub-region the facility maintenance and operations (FMO). The business models are old-fashioned and poorly support the exploitation of open data. This study presents how data sharing enables the introduction of new business models in facility maintenance and operations. Indoor conditions are one of the most critical issues in the built environment. An essential indicator of indoor conditions is perceived indoor air quality (IAQ), which problems have arisen as a national challenge in Finland during the last years. IAQ problems are difficult to control with traditional FMO processes. Also, perceived indoor conditions are challenging to verify without a dense sensor net, and preventive measures cannot be taken without field data. The aim of this study is present a new way to collect, store, analyse and utilise the field data to improve the perceived indoor conditions. Building information models (BIMs) are widely utilised in the planning and construction processes, but not much in post-construction operations. In this research, the convenient way for post-construction use of BIM is defined. Besides, the BIM-based conceptual architecture for gathering, combining, analysing, distributing, and visualising of field data has described. That solution, named as the conditions data model (CDM), improves the pace and quality of services and enables entirely new services. The CDM also renews FMO's operation models and improves IAQ's management. Besides, new kind of business emerges, and previously undefined values for owner-operators, occupants, and property service companies materialise.The relevant literature was reviewed to form the theoretical background of the study. The existence, types and sources of the field data were considered, based on the literature on human-machine interaction (HMI) and human-building interaction (HBI). Also, empirical analyses using interviews, online surveys, heuristic evaluations and studies of raw material were conducted. The grounded theory (GT) method was used to construct theory from data, using comparative analysis. Finally, the essentials were conceptualised, and conclusions were drawn using inductive inference. The most important finding is that the combination of BIM and field data, created in this study, allows a whole new way of thinking. Property maintenance is transformed from a de-tail level workflow led by the subscriber into a knowledge-based activity, where the quality of the service is the most important factor. In the new operating model, the service provider obtains all the data from the building subscriber through a common platform. Accordingly, the service provider is expected to provide high-quality service to the subscriber and occupants.
  • Machine Learning Applications for Energy Utilization of Smart Buildings
    (2022) Huotari, Matti
     School of Science | Doctoral dissertation (article-based)
    Energy utilization of smart or intelligent buildings refers to the definition, modeling, and integration of disparate energy elements into coherent energy systems in buildings with the help of artificial intelligence. A core aspect of applications for smart building energy is to address the issues of energy utilization directly while simultaneously taking into account user-comfort, security and malfunctions. Being deployed in increasing numbers in the built environment, these applications are important components of the built environment today. Given the risen number of renewable energy sources together with tightened regulation to energy consumption, the smart building energy applications provide means to combine new technology components together with heterogeneous requirements and goals for energy utilization in buildings. These goals comprise of, for instance, optimal scheduling of energy consumption and production, optimization of costs, integration of renewable energy, user-behavior recognition, and consumer comfort. This research investigates smart building energy applications. This objective is pursued through four research questions which highlight the various aspects of the smart building energy applications: (i) What algorithm to utilize for forecasting the equipment degradation, and what kind of uncertainty is associated with these forecasts for battery packs? (ii) How to build a model in case of gaps or a limited number of observations of interest in data for an air handling unit? (iii) How to involve people in personal environmental comfort decisions for smart building energy applications?, and, finally, (iv) what kind of need is there for smart building energy applications, and which solutions meet these needs? Each of these issues is dealt with using novel techniques, and a related taxonomy was created, as presented in the research publications. The relevance of the proposed solutions is verified with case-studies. Overall, machine learning models solve heterogeneous problems in the field of smart building energy utilization. The results indicate that the proposed solutions can provide answers to a variety of issues regarding building energy management, smart grid, personalization, and maintenance and security.