Scalable Data-driven Modeling and Analytics for Smart Buildings

Buildings account for over 40% of the energy and 75% of the electricity usage. Thus, by reducing our energy footprint in buildings, we can improve our overall energysustainability. Further, the proliferation of networked sensors and IoT devices in recent years have enabled monitoring of buildings to provide data at various granularity. For example, smart plugs monitor appliance level usage inside the house, while solar meters monitor residential rooftop solar installations. Furthermore, smart meters record energy usage at a grid-scale. In this thesis, I argue that data-driven modeling applied to the IoT data from a smart building, at varying granularity, in association with third party data can help to understand and reduce human energy consumption. I present four data-driven modeling approaches — that use sophisticated techniques from Machine Learning, Optimization, and Time Series Analysis — applied at different granularities. First, I study IoT devices inside the house and discuss an approach called NIMD that au- tomatically models individual electrical loads found in a household. The analytical model resulting from this approach can be used in several applications. For example, these models can improve the performance of NILM algorithms to disaggregate loads in a given household. Further, faulty or energy-inefficient appliances can be identified by observing deviations in model parameters over its lifetime. Second, I examine data from solar meters and present a machine learning framework called SolarCast to forecast energy generation from residential rooftop installations. The predictions enable exploiting the benefits of locally-generated solar energy. Third, I employ a sensorless approach utilizing a graphical model representation to re- port city-scale photovoltaic panel health and identify anomalies in solar energy production. Immediate identification of faults maximizes the solar investment by aiding in optimal operational performance. Finally, I focus on grid-level smart meter data and use correlations between energy usage and external weather to derive probabilistic estimates of energy, which is leveraged to identify the least efficient buildings from a large population along with the underlying cause of energy inefficiency. The identified homes can be targeted for custom energy efficiency programs

Pedagogical Methods for Teaching Heterogeneous Student Groups

Under de senaste åren har det visat sig att studenter på högskolorna i Sverige utgör en heterogen grupp med allt mer skilda bakgrunder och förkunskaper. Pedagogiska metoder för undervisning av sådana grupper behövs så att alla studenter klarar utbildningen. Strategier baserade på individanpassning, projektbaserad undervisning, PBL-metod etc kan effektivisera undervisningen. En diskussion kring detta kan klargöra vilka metoder är lämpliga för undervisning av heterogena studentgrupper

Influence of Hydrogen Content on Axial Fracture Toughness Parameters of Zr-2.5Nb Pressure Tube Alloy in the Temperature Range of 306-573 K

Tubes fabricated from dilute Zr-alloys serve as miniature pressure vessels in Pressurized Heavy Water Reactors and are subjected to stress, aqueous corrosion and intense irradiation during service. Hydrogen evolved during the corrosion reaction may enter into the material and precipitate as hydride phase, which acquire platelet shaped morphology in Zr-alloys and are known to embrittle the host matrix. Since hydride embrittlement is a major life limiting factor for the components made from these alloys, several theoretical and experimental studies have been carried out to understand the influence of hydrogen/hydride on the mechanical properties in general and micromechanisms assisting crack nucleation and its propagation in the presence of hydride, in particular. For ductile materials like Zr-alloys, crack initiation follows void nucleation and its growth in the plastic zone. Nucleation of voids is associated with fracture of second phase particle or separation of matrix-precipitate interface. Hydrides are suspected to be fracture initiating sites in Zr-alloys and the presence of hydride platelets normal to tensile load significantly influences crack propagation. However, the role of hydrides in crack nucleation and its propagation and influence of temperature on the same has not been delineated clearly. In this work, influence of hydrogen and temperature on the axial fracture toughness parameters of Zr-2.5Nb pressure tube alloys is reported. The fracture toughness tests were carried out using 17 mm width curved compact tension specimens machined from gaseously hydrogen charged tube-sections and tested in the temperature range of 306 to 573 K. Metallography of the samples revealed that hydrides were predominantly oriented along axial-circumferential plane of the tube. The fracture toughness parameters like JQ, J0.15, JMax, J1.5, dJ/da, KJC and KMax were determined as per the ASTM standard E-813, with the crack length measured using direct current potential drop technique. The plane strain K values were computed from the corresponding J values. The critical crack length for catastrophic failure was determined using a numerical method, which is widely used in literature. It is observed that for a given test temperature both the fracture toughness parameters representing crack initiation, such as JQ, J0.15 and KJC and crack propagation, such as JMax, J1.5, and KMax, decrease mildly with increase in hydrogen content whereas mean dJ/da is practically unaffected by hydrogen content. Also, for a given hydrogen content crack initiation fracture toughness parameters showed large scatter with a tendency to decrease with increase in test temperature whereas the crack propagation fracture toughness parameters increased with temperature to a saturation value

Particle swarm optimization and Taguchi algorithm-based power system stabilizer-effect of light loading condition

A robust design of particle swarm optimization (PSO) and Taguchi algorithm-based power system stabilizer (PSS) is presented in this paper. It incorporates a novel concept in which Taguchi and PSO techniques are integrated for stabilization of single machine infinite bus (SMIB). The system tolerates uncertainty and imprecision to a maximum extent. The proposed controller's effectiveness is proved through experiments covering light load condition using MATLAB/Simulink platform. The performance of the system is compared without PSS and with a conventional PSS. The settling time of the optimal PSS is decreased by more than 75% to conventional PSS. The study reveals that the proposed hybrid controller offers enhanced performance with respect to settling time as well as peak overshoot of the system

A Synthesis of Hidden Subgroup Quantum Algorithms and Quantum Chemical Dynamics

We describe a general formalism for quantum dynamics and show how this formalism subsumes several quantum algorithms including the Deutsch, Deutsch-Jozsa, Bernstein-Vazirani, Simon, and Shor algorithms as well as the conventional approach to quantum dynamics based on tensor networks. The common framework exposes similarities among quantum algorithms and natural quantum phenomena: we illustrate this connection by showing how the correlated behavior of protons in water wire systems that are common in many biological and materials systems parallels the structure of Shor's algorithm

Coupled diffusion-deformation multiphase field model for elastoplastic materials applied to the growth of Cu6Sn5

A coupled diffusion-deformation, multiphase field model for elastoplastic materials is presented. The equations governing the evolution of the phase fields and the molar concentration field are derived in a thermodynamically consistent way using microforce balance laws. As an example of its capabilities, the model is used to study the growth of the intermetallic compound (IMC) Cu6Sn5 during room-temperature aging. This IMC is of great importance in, e.g., soldering of electronic components. The model accounts for grain boundary diffusion between IMC grains and plastic deformation of the microstructure. A plasticity model with hardening, based on an evolving dislocation density, is used for the Cu and Sn phases. Results from the numerical simulations suggest that the thickness of the IMC layer increases linearly with time and that the morphology of the IMC gradually changes from scallop-like to planar, consistent with previous experimental findings. The model predicts that plastic deformation occurs in both the Cu and the Sn layers. Furthermore, the mean value of the biaxial stress in the Sn layer is found to saturate at a level of −8 MPa to −10 MPa during aging. This is in good agreement with experimental data