4 research outputs found
Synthesizing Action Sequences for Modifying Model Decisions
When a model makes a consequential decision, e.g., denying someone a loan, it
needs to additionally generate actionable, realistic feedback on what the
person can do to favorably change the decision. We cast this problem through
the lens of program synthesis, in which our goal is to synthesize an optimal
(realistically cheapest or simplest) sequence of actions that if a person
executes successfully can change their classification. We present a novel and
general approach that combines search-based program synthesis and test-time
adversarial attacks to construct action sequences over a domain-specific set of
actions. We demonstrate the effectiveness of our approach on a number of deep
neural networks
Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms
We propose in this paper an optimal control framework for renewable energy
communities (RECs) equipped with controllable assets. Such RECs allow its
members to exchange production surplus through an internal market. The
objective is to control their assets in order to minimise the sum of individual
electricity bills. These bills account for the electricity exchanged through
the REC and with the retailers. Typically, for large companies, another
important part of the bills are the costs related to the power peaks; in our
framework, they are determined from the energy exchanges with the retailers. We
compare rule-based control strategies with the two following control
algorithms. The first one is derived from model predictive control techniques,
and the second one is built with reinforcement learning techniques. We also
compare variants of these algorithms that neglect the peak power costs. Results
confirm that using policies accounting for the power peaks lead to a
significantly lower sum of electricity bills and thus better control strategies
at the cost of higher computation time. Furthermore, policies trained with
reinforcement learning approaches appear promising for real-time control of the
communities, where model predictive control policies may be computationally
expensive in practice. These findings encourage pursuing the efforts toward
development of scalable control algorithms, operating from a centralised
standpoint, for renewable energy communities equipped with controllable assets.Comment: 13 pages (excl. appendices and references), 14 pages of appendix. 10
figures and 10 tables. To be reviewed as a journal pape