Predictive Data Analytics for Energy Demand Flexibility

Open Access
Author:
Description:
The depleting fossil fuel and environmental concerns have created a revolutionary movement towards the installation and utilization of Renewable Energy Sources (RES) such as wind and solar energy. The RES entails challenges, both in regards to the physical integration into a grid system and regarding management of the expected demand. The flexibility in energy demand can facilitate the alignment of the supply and demand to achieve a dynamic Demand Response (DR). The flexibility is often not explicitly available or provided by a user and has to be analyzed and extracted automatically from historical consumption data. The predictive analytics of consumption data can reveal interesting patterns and periodicities that facilitate the effective extraction and representation of flexibility. The device-level analysis captures the atomic flexibilities in energy demand and provides 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 analyses 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 project is to develop a cost-effective, market-based system that utilizes total flexibility in energy demand, and provide financial and environmental benefits to all involved parties. The flexibilities from various devices are modeled using a unified format called a flex-offer, which facilitates, e.g., aggregation and trading in the energy market. In this regards, this Ph.D. study focuses on the predictive analytics of the historical device operation behavior of consumers for an efficient and ...
Contributors:
Pedersen, Torben Bach ; Lehner, Wolfgang ; Yang, Bin ; Sinn, Mathieu ; Calders, Toon ; Schill, Alexander ; Assmann, Uwe ; Technische Universität Dresden ; Aalborg University
Year of Publication:
2018-06-12
Document Type:
doc-type:doctoralThesis ; info:eu-repo/semantics/doctoralThesis ; doc-type:Text ; [Doctoral and postdoctoral thesis]
Language:
eng
Subjects:
info:eu-repo/classification/ddc/004 ; ddc:004 ; Predictive Analytics ; Demand Response ; Energieflexibilität ; Demand Flexibility
DDC:
004 Data processing & computer science ; 600 Technology (computed)
Rights:
info:eu-repo/semantics/openAccess
Relations:
URL:
https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa-236309
https://tud.qucosa.de/id/qucosa%3A31034
https://tud.qucosa.de/api/qucosa%3A31034/attachment/ATT-1/
Content Provider:
Technische Universität Dresden: Qucosa
Further nameDresden University of Technology: Qucosa  Flag of Germany