BEGIN:VCALENDAR VERSION:2.0 PRODID:www.dresden-science-calendar.de METHOD:PUBLISH CALSCALE:GREGORIAN X-MICROSOFT-CALSCALE:GREGORIAN X-WR-TIMEZONE:Europe/Berlin BEGIN:VTIMEZONE TZID:Europe/Berlin X-LIC-LOCATION:Europe/Berlin BEGIN:DAYLIGHT TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 DTSTART:19810329T030000 RRULE:FREQ=YEARLY;INTERVAL=1;BYMONTH=3;BYDAY=-1SU END:DAYLIGHT BEGIN:STANDARD TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 DTSTART:19961027T030000 RRULE:FREQ=YEARLY;INTERVAL=1;BYMONTH=10;BYDAY=-1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:DSC-13444 DTSTART;TZID=Europe/Berlin:20170927T150000 SEQUENCE:1506499438 TRANSP:OPAQUE DTEND;TZID=Europe/Berlin:20170927T160000 URL:https://dresden-science-calendar.de/calendar/de/detail/13444 LOCATION:TUD Andreas-Pfitzmann-Bau\, Nöthnitzer Straße 4601069 Dresden SUMMARY:Neupane: Predictive Data Analytics for Energy Demand Flexibility CLASS:PUBLIC DESCRIPTION:Speaker: M. Sc. Bijay Neupane\nInstitute of Speaker: Institut f ür Systemarchitektur\, Professur Datenbanken\nTopics:\nInformatik\n Locat ion:\n Name: TUD Andreas-Pfitzmann-Bau (APB 1004 (Ratssaal))\n Street: N öthnitzer Straße 46\n City: 01069 Dresden\n Phone: \n Fax: \nDescript ion: The depleting fossil fuel and environmental concerns have created a r evolutionary movement towards the installation and utilization of Renewabl e Energy Sources (RES) such as wind and solar energy. The RES entails chal lenges\, both in regards to the physical integration into a grid system an d regarding management of the expected demand. The flexibility in energy d emand can facilitate the alignment of the supply and demand to achieve a d ynamic Demand Response (DR). The flexibility is often not explicitly avail able or provided by a user and has to be analyzed and extracted automatica lly from historical consumption data. The predictive analytics of consumpt ion data can reveal interesting patterns and periodicities that facilitate the effective extraction and representation of flexibility. The device-le vel analysis captures the atomic flexibilities in energy demand and provid es the largest possible solution space to generate demand/supply schedules . The presence of stochasticity and noise in the device-level consumption data and the unavailability of contextual information makes the analytics task challenging. Hence\, it is essential to design predictive analytical techniques that work at an atomic data granularity and perform various ana lyses on the effectiveness of the proposed techniques. The Ph.D. study is sponsored by the TotalFlex Project (http://www.totalflex.dk/) and is part of the IT4BI-DC program with Aalborg University and TU Dresden as Home and Host University\, respectively. The main objective of the TotalFlex proje ct is to develop a cost-effective\, market-based system that utilizes tota l flexibility in energy demand\, and provide financial and environmental b enefits to all involved parties. The flexibilities from various devices ar e modeled using a unified format called a flex-offer\, which facilitates\, e.g.\, aggregation and trading in the energy market. In this regards\, th is Ph.D. study focuses on the predictive analytics of the historical devic e operation behavior of consumers for an efficient and effective extractio n of flexibilities in their energy demands. First\, the thesis performs a comprehensive survey of state-of-the-art work in the literature. It presen ts a critical review and analysis of various previously proposed approache s\, algorithms\, and methods in the field of user behavior analysis\, fore casting\, and flexibility analysis. Then\, the thesis details the flexibil ity and flex-offer concepts and formally discusses the terminologies used throughout the thesis. Second\, the thesis contributes to a comprehensive analysis of energy consumption behavior at the device-level. The key motiv e of the analysis is to extract device operation patterns of users\, the c orrelation between devices operations\, and influence of external factors in device-level demands. A novel cost/benefit trade-off analysis of device flexibility is performed to categorize devices into various segments acco rding to their flexibility potential. Moreover\, device-specific data prep rocessing steps are proposed to clean device-level raw data into a format suitable for flexibility analysis. Third\, the thesis presents various pre diction models that are specifically tuned for device-level energy demand prediction. Further\, it contributes to the feature engineering aspect of generating additional features from a demand consumption timeseries that e ffectively capture device operation preferences and patterns. The demand p redictions utilize the carefully crafted features and other contextual inf ormation to improve the performance of the prediction models. Further\, va rious demand prediction models are evaluated to determine the model\, fore cast horizon\, and data granularity best suited for the device-level flexi bility analysis. Furthermore\, the effect of the forecast accuracy on flex ibility-based DR is evaluated to identify an error level a market can abso rb maintaining profitability. Fourth\, the thesis proposes a generalized p rocess for automated generation and evaluation of flex-offers from the thr ee types of household devices\, namely Wet-devices\, Electric Vehicles (EV )\, and Heat Pumps. The proposed process automatically predicts and estima tes times and values of device-specific events representing flexibility in its operations. The predicted events are combined to generate flex-offers for the device future operations. Moreover\, the actual flexibility poten tial of household devices is quantified for various contextual conditions and degree days. Fifth\, the thesis presents user-comfort oriented prescri ptive techniques to prescribe flex-offers schedules. The proposed schedule r considers the trade-off between both social and financial aspects during scheduling of flex-offers\, i.e.\, maximizing the financial benefits in a market and at the same time minimizing the loss of user comfort. Moreover \, it also provides a distance-aware error measure that quantifies the act ual performance of forecast models designed for flex-offers generation and scheduling. Sixth\, the thesis contributes to the comprehensive analysis of the financial viability of device-level flexibility for dynamic balanci ng of demand and supply. The thesis quantifies the financial benefits of f lexibility and investigates the device type specific market that maximizes the potential of flexibility\, both regarding DR and financial incentives . Henceforth\, a financial analysis of each proposed technique\, namely fo recast model\, flex-offer generation model\, and flex-offer scheduling is performed. The key motive is to evaluate the usability of the proposed mod els in the device-level flexibility based DR scheme and their potential in generating a positive financial incentive to markets and customers. Seven \, the thesis presents a benchmark platform for device-level demand predic tion. The platform provides the research community with a centralized repo sitory of device-level datasets\, forecast models\, and functionalities th at facilitate comparisons\, evaluations\, and validation of device-level f orecast models. The results of the thesis can contribute to the energy mar ket in materializing the vision of utilizing consumption and production fl exibility to obtain dynamic energy balance. The developed demand forecast and flex-offer generation models also contribute to the energy data analyt ics and data mining fields. The quantification of flexibility further cont ributes by demonstrating the feasibility and financial benefits of flexibi lity-based DR. The developed experimental platform provide researchers and practitioners with the resources required for device-level demand analyti cs and prediction. DTSTAMP:20260427T130222Z CREATED:20170914T075743Z LAST-MODIFIED:20170927T080358Z END:VEVENT END:VCALENDAR