Electricity market price predictions enable energy market participants to shape their consumption or supply while meeting their economic and environmental objectives. By utilizing the basic properties of the supply-demand matching process performed by grid operators, we develop a method to recover energy market's structure and predict the resulting nodal prices as a function of generation mix and system load on the grid. Our methodology uses the latest advancements in compressed sensing and statistics to cope with the high-dimensional and sparse power grid topologies, underlying physical laws, as well as scarce, public market data. Rigorous validations using Southwest Power Pool (SPP) market data demonstrate significantly higher accuracy of the proposed approach when compared to the state-of-the-art industry benchmark.

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Keywords Locational Marginal Price (LMP), Electricity price forecast, Wholesale energy markets, Statistical learning, Big data, Compressed sensing
Stakeholder Google, San Bruno, CA, USA
Radovanovic, A, Nesti, T, & Chen, B. (2018). A holistic approach to forecasting wholesale energy market prices.