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

TL;DR

This paper introduces a deep learning model that integrates exogenous variables like load and weather data to improve day-ahead locational marginal price forecasts, especially during price valleys, aiding power generators' decision-making.

Contribution

The paper presents a novel deep learning approach that incorporates exogenous variables for more accurate daLMP forecasting, outperforming traditional methods.

Findings

Model outperforms traditional time series techniques.

Supports risk-based shutdown decision analysis.

Improves accuracy during price valleys.

Abstract

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.