Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Modeling German Energy Market Hourly Profiles with a Focus on Variable Renewable Energy

Pegoraro, Thomas LU and Ball, Vincent (2022) DABN01 20221
Department of Economics
Abstract
This paper investigates the best methods for modeling hourly profiles in the
German energy market for the period between 2018 and 2022. Modeling emphasized
variable renewable energy (VRE) and included information on the level of energy
production, oil price, COVID lockdowns, and historic hourly energy spot prices.
Previous research on energy prices has focused on interpretable models; while
investigations emphasizing predictive accuracy are sparse and sequestered in
industry. This paper is intended to contribute to the understanding of which algorithms
and what variables (endogenous and exogenous to the energy market) are best at
decreasing the discrepancies between predicted and observed hourly electricity
prices.

Four... (More)
This paper investigates the best methods for modeling hourly profiles in the
German energy market for the period between 2018 and 2022. Modeling emphasized
variable renewable energy (VRE) and included information on the level of energy
production, oil price, COVID lockdowns, and historic hourly energy spot prices.
Previous research on energy prices has focused on interpretable models; while
investigations emphasizing predictive accuracy are sparse and sequestered in
industry. This paper is intended to contribute to the understanding of which algorithms
and what variables (endogenous and exogenous to the energy market) are best at
decreasing the discrepancies between predicted and observed hourly electricity
prices.

Four different algorithms were investigated for modeling, linear regression,
lasso regression, gradient boosted trees, and a feed forward neural network. Gradient
boosted trees accounted for the most variation with an R-squared of 87.7% and
promising results on periods of high volatility. Oil price and the share of electricity
generated by solar and wind were found to substantially improve predictive accuracy,
while COVID lockdowns were less important for prediction. The results from this paper
can be used to improve hourly energy price prediction or for comparison by future
researchers on different methods. (Less)
Please use this url to cite or link to this publication:
author
Pegoraro, Thomas LU and Ball, Vincent
supervisor
organization
course
DABN01 20221
year
type
H1 - Master's Degree (One Year)
subject
keywords
Machine Learning, VRE, LCOE, Hourly Profiles, Hourly Price Forward Curves (HPFC)
language
English
id
9085053
date added to LUP
2022-06-08 12:51:36
date last changed
2022-06-08 12:51:36
@misc{9085053,
  abstract     = {{This paper investigates the best methods for modeling hourly profiles in the
German energy market for the period between 2018 and 2022. Modeling emphasized
variable renewable energy (VRE) and included information on the level of energy
production, oil price, COVID lockdowns, and historic hourly energy spot prices.
Previous research on energy prices has focused on interpretable models; while
investigations emphasizing predictive accuracy are sparse and sequestered in
industry. This paper is intended to contribute to the understanding of which algorithms
and what variables (endogenous and exogenous to the energy market) are best at
decreasing the discrepancies between predicted and observed hourly electricity
prices.

Four different algorithms were investigated for modeling, linear regression,
lasso regression, gradient boosted trees, and a feed forward neural network. Gradient
boosted trees accounted for the most variation with an R-squared of 87.7% and
promising results on periods of high volatility. Oil price and the share of electricity
generated by solar and wind were found to substantially improve predictive accuracy,
while COVID lockdowns were less important for prediction. The results from this paper
can be used to improve hourly energy price prediction or for comparison by future
researchers on different methods.}},
  author       = {{Pegoraro, Thomas and Ball, Vincent}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Modeling German Energy Market Hourly Profiles with a Focus on Variable Renewable Energy}},
  year         = {{2022}},
}