  We analyse the drivers of European Power Exchange (EPEX) wholesale
electricity prices between 2012 and early 2022 using machine learning. The
agnostic random forest approach that we use is able to reduce in-sample root
mean square errors (RMSEs) by around 50% when compared to a standard linear
least square model. This indicates that non-linearities and interaction effects
are key in wholesale electricity markets. Out-of-sample prediction errors using
machine learning are (slightly) lower than even in-sample least square errors
using a least square model. The effects of efforts to limit power consumption
and green the energy matrix on wholesale electricity prices are first order.
CO2 permit prices strongly impact electricity prices, as do the prices of
source energy commodities. And carbon permit prices impact has clearly
increased post-2021 (particularly for baseload prices). Among energy sources,
natural gas has the largest effect on electricity prices. Importantly, the role
of wind energy feed-in has slowly risen over time, and its impact is now
roughly on par with that of coal.
