Search for dissertations about: "data science in energy"
Showing result 1 - 5 of 590 swedish dissertations containing the words data science in energy.
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1. Urban Building Energy Modeling for Retrofit Scenarios : Development, Calibration, Validation and Implementation for Swedish Residential Buildings
Abstract : The necessity for an accelerated transition of urban energy systems and, in particular, the building sector toward energy efficiency and carbon neutrality poses new challenges to planning and retrofitting existing buildings. To cope with these challenges, so-called urban building energy models (UBEMs) have been introduced for quantifying the energy demand in the building sector, identifying the hot spots of energy use and suggesting scenarios for retrofitting the buildings. READ MORE
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2. Submerged Transmission in Wave Energy Converters : Full Scale In-Situ Experimental Measurements
Abstract : Different wave power technologies are in development around the world in different stages of prototype testing. So far only a few devices have been deployed offshore at full scale for extended periods of time. Little data is published about how these different devices perform. READ MORE
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3. Energy Efficiency in Machine Learning : Approaches to Sustainable Data Stream Mining
Abstract : Energy efficiency in machine learning explores how to build machine learning algorithms and models with low computational and power requirements. Although energy consumption is starting to gain interest in the field of machine learning, still the majority of solutions focus on obtaining the highest predictive accuracy, without a clear focus on sustainability. READ MORE
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4. Efficient improvement of energy efficiency in small and medium- sized Swedish firms
Abstract : This is a dissertation about efficient implementation of energy efficiency measures in small and medium-sized Swedish firms. The aim is to investigate the potential for economically efficient implementation of energy efficiency improvement measures in small and medium-sized firms. READ MORE
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5. How can data science contribute to a greener world? : an exploration featuring machine learning and data mining for environmental facilities and energy end users
Abstract : Human society has taken many measures to address environmental issues. For example, deploying wastewater treatment plants (WWTPs) to alleviate water pollution and the shortage of usable water; using waste-to-energy (WtE) plants to recover energy from the waste and reduce its environmental impact. READ MORE