Online Peak-Aware Energy Scheduling with Untrusted Advice
Online Peak-Aware Energy Scheduling with Untrusted Advice
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DOI：
10.1145/3447555.3464860
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Russell Lee;Jessica Maghakian;M. Hajiesmaili;Jian Li;R. Sitaraman;Zhenhua Liu
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文献类型：
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作者：
Russell Lee;Jessica Maghakian;M. Hajiesmaili;Jian Li;R. Sitaraman;Zhenhua Liu
本文研究了在混合模型中的在线能源调度问题，其中能源成本与数量和峰值使用成正比，并且可以从网格中局部产生或从网格中获取能量。受到机器学习（ML）建议的在线算法的最新进展的启发，我们为此问题开发了参数化的确定性和随机算法，以便可以通过信任参数调整对建议的依赖水平。然后，我们使用两个性能指标分析提出的算法的性能：鲁棒性，当建议不准确时，在建议准确时，衡量竞争比与信任参数的函数，当建议准确时竞争比率的一致性。由于在两个不同的制度中分析了竞争比率，因此我们进一步研究了拟议算法的帕累托最优性。我们的结果表明，所提出的确定性算法是帕累托最佳的，因为没有其他在线确定性算法可以主导我们算法的鲁棒性和一致性。此外，我们表明所提出的随机算法主导了帕累托最佳的确定性算法。我们使用能源需求，能源价格和可再生能源世代的真实痕迹的大规模经验评估强调，拟议算法的表现优于最差的优化算法和完全数据驱动的算法。
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 outperform worst-case optimized algorithms and fully data-driven algorithms.