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Title: Online peak-aware energy scheduling with untrusted advice
This paper studies the online energy scheduling problem in a hybrid model where the cost of energy is proportional to both the volume and peak usage, and where energy can be either locally generated or drawn from the grid. Inspired by recent advances in online algorithms with Machine Learned (ML) advice, we develop parameterized deterministic and randomized algorithms for this problem such that the level of reliance on the advice can be adjusted by a trust parameter. We then analyze the performance of the proposed algorithms using two performance metrics: robustness that measures the competitive ratio as a function of the trust parameter when the advice is inaccurate, and consistency for competitive ratio when the advice is accurate. Since the competitive ratio is analyzed in two different regimes, we further investigate the Pareto optimality of the proposed algorithms. Our results show that the proposed deterministic algorithm is Pareto-optimal, in the sense that no other online deterministic algorithms can dominate the robustness and consistency of our algorithm. Furthermore, we show that the proposed randomized algorithm dominates the Pareto-optimal deterministic algorithm. Our large-scale empirical evaluations using real traces of energy demand, energy prices, and renewable energy generations highlight that the proposed algorithms more » outperform worst-case optimized algorithms and fully data-driven algorithms. « less
Authors:
; ; ; ; ;
Award ID(s):
2106299 2045641 2105494 1908298 2136199
Publication Date:
NSF-PAR ID:
10349527
Journal Name:
ACM SIGEnergy Energy Informatics Review
Volume:
1
Issue:
1
Page Range or eLocation-ID:
59 to 77
ISSN:
2770-5331
Sponsoring Org:
National Science Foundation
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