Forecasting Electricity Prices
- URL: http://arxiv.org/abs/2204.11735v1
- Date: Mon, 25 Apr 2022 15:46:26 GMT
- Title: Forecasting Electricity Prices
- Authors: Katarzyna Maciejowska, Bartosz Uniejewski, Rafa{\l} Weron
- Abstract summary: Power system stability calls for a constant balance between production and consumption.
The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities.
There is a shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend.
Case studies compare profits from scheduling or trading strategies based on price forecasts obtained from different models.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Forecasting electricity prices is a challenging task and an active area of
research since the 1990s and the deregulation of the traditionally monopolistic
and government-controlled power sectors. Although it aims at predicting both
spot and forward prices, the vast majority of research is focused on short-term
horizons which exhibit dynamics unlike in any other market. The reason is that
power system stability calls for a constant balance between production and
consumption, while being weather (both demand and supply) and business activity
(demand only) dependent. The recent market innovations do not help in this
respect. The rapid expansion of intermittent renewable energy sources is not
offset by the costly increase of electricity storage capacities and
modernization of the grid infrastructure. On the methodological side, this
leads to three visible trends in electricity price forecasting research as of
2022. Firstly, there is a slow, but more noticeable with every year, tendency
to consider not only point but also probabilistic (interval, density) or even
path (also called ensemble) forecasts. Secondly, there is a clear shift from
the relatively parsimonious econometric (or statistical) models towards more
complex and harder to comprehend, but more versatile and eventually more
accurate statistical/machine learning approaches. Thirdly, statistical error
measures are nowadays regarded as only the first evaluation step. Since they
may not necessarily reflect the economic value of reducing prediction errors,
more and more often, they are complemented by case studies comparing profits
from scheduling or trading strategies based on price forecasts obtained from
different models.
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