Weather Condition Clustering for Improvement of Photovoltaic Power Plant Generation Forecasting Accuracy  
[PERSON]  
Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia, [PERSON], 19 Mira Str., Vekaterinburg 620062, Russia; [EMAIL_ADDRESS] (K.I.H. ); [EMAIL_ADDRESS] (A.M.B. ); [EMAIL_ADDRESS] (S.A.E.) [PERSON]  
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###### Abstract  
Together with the growing interest towards renewable energy sources within the framework of different strategies of various countries, the number of solar power plants keeps growing. However, managing optimal power generation for solar power plants has its own challenges. First comes the problem of work interruption and reduction in power generation. As the system must be tolerant to the faults, the relevance and significance of short-term forecasting of solar power generation becomes crucial. Within the framework of this research, the applicability of different forecasting methods for short-time forecasting is explained. The main goal of the research is to show an approach regarding how to make the forecast more accurate and overcome the above-mentioned challenges using opensource data as features. The data clustering algorithm based on KMeans is proposed to train unique models for specific groups of data samples to improve the generation forecast accuracy. Based on practical calculations, machine learning models based on Random Forest algorithm are selected which have been proven to have higher efficiency in predicting the generation of solar power plants. The proposed algorithm was successfully tested in practice, with an achieved accuracy near to 90%. solar power plant; power generation forecasting; machine learning; weather clustering +
Footnote †: journal: _Algorithms_  
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Over the last 10 years, the share of renewable electricity has significantly grown. Today, the highest rates are seen in the capacities of solar and wind power [18; 19]. According to statistics from 2022, photovoltaics accounted for 50% of the new installed capacities [20]. However, this technology has its own drawbacks. Along with the growing amount of power plants, interruption of work and reduction in power generation create certain disadvantages for power systems. Grid failures may rise due to the impact of power surpluses or shortages, so it is necessary to build a renewable energy system that is tolerant to these faults. One of the ways to overcome these challenges is to ensure an efficient use of solar power. The possible tool for that is the prediction of PV power generation. To date, there are two main approaches--the physical one and the statistical one. When developing a physical model, several characteristics are taken into consideration. Those include the influence of solar radiation, the power plant itself, the PV conversion model, the circuit model and the inverter model. For example, work [21] suggests the prediction method to calculate the parameters and paper [22] proposes the linear system of five equations to assess the above-mentioned parameters. However, due to the changing parameters of the performance of PV models and other challenges, such as changing weather conditions, it becomes difficult to build a physical prediction method. In comparison to physical models, it is much easier to use a statistical approach, as it is better in catching the probable uncertainties. This method, in turn, can be split into system identification methods and artificial intelligence (AI) approaches, including support vector machines, artificial neural networks and genetic algorithms [23]. Statistical models are based on the analysis of retrospective data to predict changes in the value under consideration [24; 25; 26; 27; 28; 29]. Statistical models use methods of mathematical statistics, probability distributions, time series, autoregressive models, etc.