This paper examines performance of selected frequently used machine learning models for
the purpose of forecasting the day-ahead prices in the German power market. Data for 2017 …

Sažetak This paper examines performance of selected frequently used machine learning
models for the purpose of forecasting the day-ahead prices in the German power market …

This paper gives an overview of several models applied to forecast the day-ahead prices of
the German electricity market between 2014 and 2015 using hourly wind and solar …

In the context of the increase in the fraction of power generation coming from unpredictable
renewable sources, electricity prices are as volatile as ever. This volatility makes forecasting …

Electricity spot market prices are increasingly affected by an expanding amount of
renewables and a growing number of market participants. In an attempt to improve …

This paper evaluates the performance of alternative algorithms for day-ahead electricity
price forecasting. Forecasting performance is assessed based on evidence from the Greek …

Electricity price forecasting plays a crucial role in the optimal management of the power grid.
Hence, predicting electricity prices becomes a crucial task from the perspective of the market …

Short-term electricity price forecasting has received considerable attention in recent years.
Despite this increased interest, the literature lacks a concrete consensus on the most …

In recent years, European countries have moved from regulated regional monopolies to
liberalised electricity markets. The power market has changed from a vertically integrated …

Participants in deregulated electricity markets face risks from price volatility due to various
factors, including fuel prices, renewable energy production, electricity demand, and crises …