# Probabilistic Mid- and Long-Term Electricity Price Forecasting

## TL;DR

This paper introduces a probabilistic model for mid- and long-term hourly electricity price forecasting, addressing a gap in the literature by providing realistic, uncertainty-aware predictions over several months to years using market data.

## Contribution

It extends the X-Model to generate probabilistic, long-term electricity price forecasts with hourly resolution, incorporating uncertainty and predicting rare events like prolonged negative prices.

## Key findings

- Realistic long-term price patterns achieved
- Model detects probabilities of negative price spikes
- Promising results with common error measures

## Abstract

The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity prices for a short period of time provided new opportunities for market participants. However, the electricity price literature seem to be very scarce on the issue of medium- to long-term price forecasting, which is mandatory for investment and political decisions. Our paper closes this gap by introducing a new approach to simulate electricity prices with hourly resolution for several months up to three years. Considering the uncertainty of future events we are able to provide probabilistic forecasts which are able to detect probabilities for price spikes even in the long-run. As market we decided to use the EPEX day-ahead electricity market for Germany and Austria. Our model extends the X-Model which mainly utilizes the sale and purchase curve for electricity day-ahead auctions. By applying our procedure we are able to give probabilities for the due to the EEG practical relevant event of six consecutive hours of negative prices. We find that using the supply and demand curve based model in the long-run yields realistic patterns for the time series of electricity prices and leads to promising results considering common error measures.