Authors:
Jörg Bremer
1
and
Sebastian Lehnhoff
2
Affiliations:
1
University of Oldenburg, Germany
;
2
OFFIS – Institute for Information Technology, Germany
Keyword(s):
Uncertainty, SVDD, Smart Grid, Distributed Generation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computing
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Planning and Scheduling
;
Simulation and Modeling
;
Soft Computing
;
Symbolic Systems
;
Uncertainty in AI
Abstract:
Robust proactive planning of day-ahead real power provision must incorporate uncertainty in feasibility when trading off different schedules against each other during the predictive planning phase. Imponderabilities like weather, user interaction, projected heat demand, and many more have a major impact on feasibility – in the sense of being technically operable by a specific energy unit. Deviations from the predicted initial operational state of an energy unit may easily foil a planned schedule commitment and provoke the need for ancillary services. In order to minimize control power and cost arising from deviations from agreed energy product delivery, it is advantageous to a priori know about individual uncertainty. We extend an existing surrogate model that has been successfully used in energy management for checking feasibility during constraint-based optimization. The surrogate is extended to incorporate confidence scores based on expected feasibility under changed operational c
onditions. We demonstrate the superiority of the new surrogate model by results from several simulation studies.
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