Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/263737 
Year of Publication: 
2022
Series/Report no.: 
CESifo Working Paper No. 9807
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
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 − indicating 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.
Subjects: 
carbon permit
CO2 emissions
commodities
electricity market
energy
EPEX
machine learning
natural gas
oil
wind energy
JEL: 
C54
D40
L70
Q02
Q20
Q40
Document Type: 
Working Paper
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