  In this paper, we study the peak-aware energy scheduling problem using the
competitive framework with machine learning prediction. With the uncertainty of
energy demand as the fundamental challenge, the goal is to schedule the energy
output of local generation units such that the electricity bill is minimized.
While this problem has been tackled using classic competitive design with
worst-case guarantee, the goal of this paper is to develop learning-assisted
competitive algorithms to improve the performance in a provable manner. We
develop two deterministic and randomized algorithms that are provably robust
against the poor performance of learning prediction, however, achieve the
optimal performance as the error of prediction goes to zero. Extensive
experiments using real data traces verify our theoretical observations and show
15.13% improved performance against pure online algorithms.

Data Structures and Algorithms;Systems and Control;Systems and Control