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Several approaches have been proposed to forecast day-ahead locationalmarginal price (daLMP) in deregulated energy markets. The rise of deep learninghas motivated its use in energy price forecasts but most deep learningapproaches fail to accommodate for exogenous variables, which have significantinfluence in the peaks and valleys of the daLMP. Accurate forecasts of thedaLMP valleys are of crucial importance for power generators since one of themost important decisions they face is whether to sell power at a loss toprevent incurring in shutdown and start-up costs, or to bid at production costand face the risk of shutting down. In this article we propose a deep learningmodel that incorporates both the history of daLMP and the effect of exogenousvariables (e.g., forecasted load, weather data). A numerical study at the PJMindependent system operator (ISO) illustrates how the proposed modeloutperforms traditional time series techniques while supporting risk-basedanalysis of shutdown decisions.

A Deep Learning Forecaster with Exogenous Variables for Day-Ahead Locational Marginal Price