Electricity Day-Ahead Market Conditions and Their Effect on the Different Supervised Algorithms for Market Price Forecasting
نویسندگان
چکیده
Participants in deregulated electricity markets face risks from price volatility due to various factors, including fuel prices, renewable energy production, demand, and crises such as COVID-19 energy-related issues. Price forecasting is used mitigate risk trading goods which have high volatility. Forecasting difficult challenging attributed many unpredictable factors. This work studies reports the performance both terms of error computational time algorithms that are based on Extreme Learning Machine, Artificial Neural Network, XGBoost random forest. All these machine learning techniques combined with Bootstrap technique creating new samples available ones order improve errors. In assess methodologies, Day-Ahead market prices divided into three classes, namely normal, extremely negative, subsequently provide forecasts for whole year 2020 German Finnish markets. The average yearly errors along computation required by each methodology reported. findings indicate forest algorithm performs best normal categories, while demonstrates better results negative category. Machine requires least achieves comparable best-performing methods.
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ژورنال
عنوان ژورنال: Energies
سال: 2023
ISSN: ['1996-1073']
DOI: https://doi.org/10.3390/en16124617