  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, known as Optimal
Power Flow (OPF), we develop a methodology to recover energy market's structure
and predict the resulting nodal prices by using only publicly available data,
specifically grid-wide generation type mix, system load, and historical prices.
Our methodology uses the latest advancements in statistical learning to cope
with high dimensional and sparse real power grid topologies, as well as scarce,
public market data, while exploiting structural characteristics of the
underlying OPF mechanism. Rigorous validations using the Southwest Power Pool
(SPP) market data reveal a strong correlation between the grid level mix and
corresponding market prices, resulting in accurate day-ahead predictions of
real time prices. The proposed approach demonstrates remarkable proximity to
the state-of-the-art industry benchmark while assuming a fully decentralized,
market-participant perspective. Finally, we recognize the limitations of the
proposed and other evaluated methodologies in predicting large price spike
values.
