Electricity price forecasting : a deep learning approach

Abstract

Locational marginal pricing (LMP) is a pricing mechanism used in electricity transmission systems which reflects price differentials based upon locational availability and system constraints. If a load in the system cannot meet its demand from the cheapest available generation sources, then it must draw power from more expensive sources, causing a price differential, also called congestion. Many electric transmission systems around the world have adopted this policy in order to reflect this reality and create a more transparent pricing environment. Electricity price forecasting (EPF) is used to make several important economic decisions across the grid, both for generation and load entities, including bidding, trading, and arbitrage. EPF has been studied extensively for the past twenty years, the most successful models relying on multilayer perceptrons (MLPs) or recurrent neural networks, but only focus on univariate time series. With the plethora of data available in the EPF setting, new developments in deep learning can leverage multivariate relationships and improve upon simpler models used in the past. In this report, we employ a modification of the WaveNet architecture for electricity price forecasting of the Day-Ahead-Electricity Market (DAM) in the Electricity Reliability Council of Texas (ERCOT) grid.