Modelling of Electrical Appliance Signatures for Energy Disaggregation

The rapid development of technology in the electrical sector within the last 20 years has led to growing electric power needs through the increased number of electrical appliances and automation of tasks. In contrary, reduction of the overall energy consumption as well as efficient energy management are needed, in order to reduce global warming and meet the global climate protection goals. These requirements have led to the recent adoption of smart-meters and smart-grids, as well as to the rise of Non-Intrusive Load Monitoring. Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power consumption as measured by a single smart meter at the inlet of a household. Therefore, Non-Intrusive Load Monitoring is a highly under-determined problem which aims to estimate multiple variables from a single observation, thus is impossible to be solved analytical. In order to find accurate estimates of the unknown variables three fundamentally different approaches, namely deep-learning, pattern matching and single-channel source separation, have been investigated in the literature in order to solve the Non-Intrusive Load Monitoring problem. While Non-Intrusive Load Monitoring has multiple areas of application, including energy reduction through consumer awareness, load scheduling for energy cost optimization or reduction of peak demands, the focus of this thesis is especially on the performance of the disaggregation algorithm, the key part of the Non-Intrusive Load Monitoring architecture. In detail, optimizations are proposed for all three architectures, while the focus lies on deep-learning based approaches. Furthermore, the transferability capability of the deep-learning based approach is investigated and a NILM specific transfer architecture is proposed. The main contribution of the thesis is threefold. First, with Non-Intrusive Load Monitoring being a time-series problem incorporation of temporal information is crucial for accurate modelling of the appliance signatures and the change of signatures over time. Therefore, previously published architectures based on deep-learning have focused on utilizing regression models which intrinsically incorporating temporal information. In this work, the idea of incorporating temporal information is extended especially through modelling temporal patterns of appliances not only in the regression stage, but also in the input feature vector, i.e. by using fractional calculus, feature concatenation or high-frequency double Fourier integral signatures. Additionally, multi variance matching is utilized for Non-Intrusive Load Monitoring in order to have additional degrees of freedom for a pattern matching based solution. Second, with Non-Intrusive Load Monitoring systems expected to operate in realtime as well as being low-cost applications, computational complexity as well as storage limitations must be considered. Therefore, in this thesis an approximation for frequency domain features is presented in order to account for a reduction in computational complexity. Furthermore, investigations of reduced sampling frequencies and their impact on disaggregation performance has been evaluated. Additionally, different elastic matching techniques have been compared in order to account for reduction of training times and utilization of models without trainable parameters. Third, in order to fully utilize Non-Intrusive Load Monitoring techniques accurate transfer models, i.e. models which are trained on one data domain and tested on a different data domain, are needed. In this context it is crucial to transfer time-variant and manufacturer dependent appliance signatures to manufacturer invariant signatures, in order to assure accurate transfer modelling. Therefore, a transfer learning architecture specifically adapted to the needs of Non-Intrusive Load Monitoring is presented. Overall, this thesis contributes to the topic of Non-Intrusive Load Monitoring improving the performance of the disaggregation stage while comparing three fundamentally different approaches for the disaggregation problem

Geosciences Roadmap for Research Infrastructures 2025 - 2028 by the Swiss Geosciences Community

This roadmap is the product of a grassroots effort by the Swiss Geosciences community. It is the first of its kind, outlining an integrated approach to research facilities for the Swiss Geosciences. It spans the planning period 2025-2028. Swiss Geoscience is by its nature leading or highly in-volved in research on many of the major national and global challenges facing society such as climate change and meteorological extreme events, environmental pol-lution, mass movements (land- and rock-slides), earth-quakes and seismic hazards, global volcanic hazards, and energy and other natural resources. It is essential to under- stand the fundamentals of the whole Earth system to pro-vide scientific guidelines to politicians, stakeholders and society for these pressing issues. Here, we strive to gain efficiency and synergies through an integrative approach to the Earth sciences. The research activities of indivi- dual branches in geosciences were merged under the roof of the 'Integrated Swiss Geosciences'. The goal is to facilitate multidisciplinary synergies and to bundle efforts for large research infrastructural (RI) requirements, which will re-sult in better use of resources by merging sectorial acti- vities under four pillars. These pillars represent the four key RIs to be developed in a synergistic way to improve our understanding of whole-system processes and me- chanisms governing the geospheres and the interactions among their components. At the same time, the roadmap provides for the required transition to an infrastructure adhering to FAIR (findable, accessible, interoperable, and reusable) data principles by 2028.The geosciences as a whole do not primarily profit from a single large-scale research infrastructure investment, but they see their highest scientific potential for ground-break-ing new findings in joining forces in establishing state-of-the-art RI by bringing together diverse expertise for the benefit of the entire geosciences community. Hence, the recommendation of the geoscientific community to policy makers is to establish an integrative RI to support the ne- cessary breadth of geosciences in their endeavor to ad-dress the Earth system across the breadth of both temporal and spatial scales. It is also imperative to include suffi-cient and adequately qualified personnel in all large RIs. This is best achieved by fostering centers of excellence in atmospheric, environmental, surface processes, and deep Earth projects, under the roof of the 'Integrated Swiss Geosciences'. This will provide support to Swiss geo-sciences to maintain their long standing and internatio- nally well-recognized tradition of observation, monitor-ing, modelling and understanding of geosciences process-es in mountainous environments such as the Alps and beyond