  This paper develops learning-augmented algorithms for energy trading in
volatile electricity markets. The basic problem is to sell (or buy) $k$ units
of energy for the highest revenue (lowest cost) over uncertain time-varying
prices, which can framed as a classic online search problem in the literature
of competitive analysis. State-of-the-art algorithms assume no knowledge about
future market prices when they make trading decisions in each time slot, and
aim for guaranteeing the performance for the worst-case price sequence. In
practice, however, predictions about future prices become commonly available by
leveraging machine learning. This paper aims to incorporate machine-learned
predictions to design competitive algorithms for online search problems. An
important property of our algorithms is that they achieve performances
competitive with the offline algorithm in hindsight when the predictions are
accurate (i.e., consistency) and also provide worst-case guarantees when the
predictions are arbitrarily wrong (i.e., robustness). The proposed algorithms
achieve the Pareto-optimal trade-off between consistency and robustness, where
no other algorithms for online search can improve on the consistency for a
given robustness. Further, we extend the basic online search problem to a more
general inventory management setting that can capture storage-assisted energy
trading in electricity markets. In empirical evaluations using traces from
real-world applications, our learning-augmented algorithms improve the average
empirical performance compared to benchmark algorithms, while also providing
improved worst-case performance.
