Machine Learning for Smart and Energy-Efficient Buildings

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning

Evaluating and Optimizing Distributed Energy Resources

Climate change is one of the most urgent problems faced by humanity, and rising sea levels, extreme weather events and desertification pose a severe threat to human life as we know it. Greenhouse gas emissions resulting from human activities have caused long-term global warming, and are primarily caused by the burning of fossil fuels to generate energy, e.g., for electricity, heat or transport. It is essential that we move to cleaner sources of energy across the board to prevent further greenhouse gas emissions, and this move is driven by three main trends. First, the rise of distributed energy resources through rooftop solar, backup batteries and electric vehicles has led to the creation of a new class of consumers which have electricity production capability. These resources tend to be variable, and are owned and operated independently. Second, the move to variable clean energy production will lead to power system operators having an increased need for demand side flexibility in order to accommodate the supply-side variability. Flexible consumers can bid in their flexibility into markets for profit, or use it to reduce their emissions impact. Third, the increasing share of electric vehicles will led to an intersection of the transportation and power networks, where electric vehicles will be able to use their batteries as `mobile' storage in the power network. This dissertation addresses some key challenges associated with each of these trends, and proposes solutions for them

Time Varying Marginal Emissions Intensity of Energy Consumption: Implications for Flexible Loads

Climate-conscious electricity consumers can modify their energy consumption patterns by shifting or shedding load in order to reduce carbon emissions. The impact of modified consumption on emissions is through the marginal emissions intensity, i.e., the emissionality of the marginal resource on the grid. The marginal emissions intensity varies with time, and we study the peaks and differences of this intensity over 2018. We find that alongside a seasonal pattern in emissions intensity, there are some times when it is particularly effective to shed or shift load. This has implications for situations when modifying consumption is expensive or causes discomfort to consumers, as it can help prioritise load shift/shed actions. We study the energy consumption patterns during these time periods and identify loads that might be particularly useful for load shed/shift