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It is indisputable that power systems are being transformed around the world to increase the use of RES and reduce the use of fossil fuels in overall electricity production. This year, the EU Parliament adopted the Fit for 55 package, which should significantly reduce the use of fossil fuels in the energy balance of EU countries while increasing the use of RES. At the end of 2022, the total number of prosumer installations in Poland amounted to about one million two hundred thousand. Such a high saturation of prosumer micro-installations in the power system causes many threats resulting from their operation. These threats result, among others, from the fact that photovoltaics are classified as unstable sources and the expected production of electricity from such installations is primarily associated with highly variable weather conditions and is only dependent on people to a minor extent. Currently, there is a rapid development of topics related to forecasting the volume of energy production from unstable sources such as wind and photovoltaic power plants. This issue is being actively developed by research units around the world. Scientists use a whole range of tools and models related to forecasting techniques, from physical models to artificial intelligence. According to our findings, the use of machine learning models has the greatest chance of obtaining positive prognostic effects for small, widely distributed prosumer installations. The present paper presents the research results of two energy balance prediction algorithms based on machine learning models. For forecasting, we proposed two regression models, i.e., regularized LASSO regression and random forests. The work analyzed scenarios taking into account both endogenous and exogenous variables as well as direct multi-step forecasting and recursive multi-step forecasting. The training was carried out on real data obtained from a prosumer micro-installation. Finally, it was found that the best forecasting results are obtained with the use of a random forest model trained using a recursive multi-step method and an exogenous scenario. [ABSTRACT FROM AUTHOR]

Prediction of considerable wind power is a signifi cant factor in modern power systems' robust and resilient operation. As a result, many studies addressed up-to-day-ahead wind power forecasting. Taking into account the abilities of machine learning (ML) Models and their combinations, in this paper, the Authors would like to present the framework for 60-hour wind power forecasting in Poland. Possession of longer than one-day ahead of accurate wind power forecasts gives an ability to the transmission system operator (TSO) and distribution system operator (DSO) the to improve the control of grid traffi c and assimilate more effi ciently the renewable energy sources (RES) generated power. The presented method uses the geographical coordinates of wind farms in the area of interest with their wind-power curves. It combines it with weather parameters such as wind speed and direction, wind gust, air temperature, and pressure for improved accuracy. The novelty of the proposed method is that model can adapt autonomously according to achieved past accuracy. A back-score accuracy has been tracked based on past predictions and measured wind power generation. Furthermore, the model combines different ML models that can be adapted or retrained if prediction performance drops below an acceptable level. In this paper, we would like to present a method and performance analysis of the adaptive ensemble-model method, whose accuracy has been calculated compared with actual data measured in the National Power System of Poland. Furthermore, the paper's novelty is that the proposed method/framework can use only an approximate model of large wind generation at the beginning, and the model can be fi ne-tuned in the proposed method's operation as a function of back-accuracy measurement triggering ensemble-model adaptation. [ABSTRACT FROM AUTHOR]

The role of renewable energy sources in the Polish power system is growing. The highest share of installed capacity goes to wind and solar energy. Both sources are characterized by high variability of their power output and very low dispatchability. Taking into account the nature of the power system, it is, therefore, imperative to predict their future energy generation to economically schedule the use of conventional generators. Considering the above, this paper examines the possibility to predict day-ahead wind power based on different machine learning methods not for a specific wind farm but at national level. A numerical weather prediction model used operationally in the Institute of Meteorology and Water Management–National Research Institute in Poland and hourly data of recorded wind power generation in Poland were used for forecasting models creation and testing. With the best method, the Extreme Gradient Boosting, and two years of training (2018–2019), the day-ahead, hourly wind power generation in Poland in 2020 was predicted with 26.7% mean absolute percentage error and 4.5% root mean square error accuracy. Seasonal and daily differences in predicted error were found, showing high mean absolute percentage error in summer and during daytime. [ABSTRACT FROM AUTHOR]