Extreme supervised algorithm for day ahead market price forecasting

Abstract

Deregulation of electricity markets has ushered in a new era of heightened competition, allowing for the inclusion of fresh market entrants. However, market participation bears challenges related the extremely high volatility of prices that are affected by too many interrelated factors, such as weather conditions, production, consumption, renewable production, fuel prices, unexpected socio-political and health events, etc. Therefore, the electricity market shows unexpected changes that can lead to very high (extremely high) or very low levels (negative) prices, exposing the participants to high financial risks. Given the stochastic nature of electricity energy prices, this work employs the Extreme Learning Machine in conjunction with the Bootstrap method for forecasting electricity prices for the next day. Two distinct cases are examined. In the first case, the prediction model is trained only with historical market price data, while in the second one with forecast data. The reason this is done is to see which data gives better forecasts. The methodology was applied to German and Finnish market data, 2019–2022.