  Several approaches have been proposed to forecast day-ahead locational
marginal price (daLMP) in deregulated energy markets. The rise of deep learning
has motivated its use in energy price forecasts but most deep learning
approaches fail to accommodate for exogenous variables, which have significant
influence in the peaks and valleys of the daLMP. Accurate forecasts of the
daLMP valleys are of crucial importance for power generators since one of the
most important decisions they face is whether to sell power at a loss to
prevent incurring in shutdown and start-up costs, or to bid at production cost
and face the risk of shutting down. In this article we propose a deep learning
model that incorporates both the history of daLMP and the effect of exogenous
variables (e.g., forecasted load, weather data). A numerical study at the PJM
independent system operator (ISO) illustrates how the proposed model
outperforms traditional time series techniques while supporting risk-based
analysis of shutdown decisions.
