The accurate forecasting of solar power generation is becoming increasingly important in the context of renewable energy integration and intelligent energy management. The variability of solar radiation, caused by changing meteorological conditions and diurnal cycles, complicates the planning and control of photovoltaic systems and may lead to imbalances in supply and demand. This study aims to identify the most effective exponential smoothing approach for real-world PV power forecasting using actual hourly generation data from a 9 MW solar power plant in the Kyiv region, Ukraine. Four exponential smoothing techniques are analysed: Classic, a Modified classic adapted to daily generation patterns, Holt’s linear trend method, and the Holt–Winters seasonal method. The models were implemented in Microsoft Excel (Microsoft 365, version 2408) using real measurement data collected over six months. Forecasts were generated one hour ahead, and optimal smoothing constants were identified via RMSE minimisation using the Solver Add-in. Substantial differences in forecasting accuracy were observed. The Classic simple exponential smoothing model performed worst, with an RMSE of 1413.58 kW and nMAE of 9.22%. Holt’s method improved trend responsiveness (RMSE = 1052.79 kW, nMAE = 5.96%), but still lacked seasonality modelling. Holt–Winters, which incorporates both trend and seasonality, achieved a strong balance (RMSE = 1031.00 kW, nMAE = 3.7%). The best performance was observed with the modified simple exponential smoothing method, which captured the daily cycle more effectively (RMSE = 166.45 kW, nMAE = 0.84%). These results pertain to a one-step-ahead evaluation on a single plant and an extended validation window; accuracy is dependent on meteorological conditions, with larger errors during rapid cloud transi. The study identifies forecasting models that combine high accuracy with structural simplicity, intuitive implementation, and minimal parameter tuning—features that make them well-suited for integration into lightweight real-time energy control systems, despite not being evaluated in terms of runtime or memory usage. The modified simple exponential smoothing model, in particular, offers a high degree of precision and interpretability, supporting its integration into operational PV forecasting tools.

The new model of the wholesale electricity market in Ukraine causes appearance the market for the day ahead. In this market, the generating company undertakes to supply a certain amount of electricity. So, it is necessary to carry on the most accurate forecast of possible electricity generation by solar power plant (SPP). Generation value depends on certain factors. A brief summary of different influence of parameters on the PV cell performance has been provided. The article analyzes and identifies the factors that should be included in the forecast mathematical model of electricity generation by a solar power plant for a certain short-term period. According to analyzed data from SPP located in the Kyiv region, such parameters are the intensity of solar radiation, temperature and humidity, wind speed, and atmospheric pressure. The degree of influence of these factors on the initial function of electric energy generation were estimated by analyzing the scatter plot diagrams of relationship between parameters and correlation coefficients. Thus, the analysis of the influence of factors on the magnitude of electricity generation allowed to determine the priority of including each of the parameters in the mathematical model of the SPP power forecast. It was established that the influence of certain climate parameters for target function is different in each season. Therefore, in the mathematical model for forecasting electric power generation, it is necessary to take into account seasonality. In addition, the dynamic value change of factors also affects the current magnitude of electricity generation. Moreover, at different times of the year and with different combination of the corresponding values of climatic parameters, this effect may have different magnitudes. Therefore, the data obtained from the last periods before the forecasting should have a greater impact on obtaining the predicted value than the data from previous periods.

Accurate forecasting of solar power generation is becoming increasingly important in the context of renewable energy integration and intelligent energy management. The variability of solar radiation, caused by changing meteorological conditions and diurnal cycles, complicates the planning and control of photovoltaic systems and may lead to imbalances in supply and demand. This study aims to identify the most effective exponential smoothing approach for real-world PV generation forecasting using actual hourly generation data from a 9 MW solar power plant in the Kyiv region, Ukraine. Four exponential smoothing techniques are analysed: Classic, a Modified сlassic adapted to daily generation patterns, Holt’s linear trend method, and the Holt-Winters seasonal method. The models were implemented in Microsoft Excel using real measurement data collected over six months. Forecasts were generated one hour ahead, and optimal smoothing constants were identified via RMSE minimisation using the built-in Solver tool. Substantial differences in forecasting accuracy were observed. The Classic simple exponential smoothing model performed worst, with an RMSE of 1413.58 kW and nMAE of 9.22%. Holt’s method improved trend responsiveness (RMSE = 1052.79 kW, nMAE = 5.96%), but still lacked seasonality modelling. Holt-Winters, which incorporates both trend and seasonality, achieved a strong balance (RMSE = 1031.00 kW, nMAE = 3.7%). The best performance was observed with the Modified simple exponential smoothing method, which captured the daily cycle more effectively (RMSE = 166.45 kW, nMAE = 0.84%). The study identifies forecasting models that combine accuracy with low computational complexity, making them suitable for lightweight real-time energy control systems. The Modified simple exponential smoothing model, in particular, offers a high degree of precision and interpretability, supporting its integration into operational PV forecasting tools.

The article examines Ukraine`s international environmental commitments to reduce atmospheric pollutant emissions, specifically dust, sulfur oxides, and nitrogen oxides. The energy consumption volumes and pollutant emissions across economic sectors were analyzed. It was determined that the industrial sector is the largest source of pollutant emissions, with Manufacturing and Electricity, Gas, Steam, and Air Conditioning Supply leading in both emissions and energy consumption. Three main groups of measures for reducing atmospheric emissions are identified: improving the energy efficiency and energy conservation to achieve fuel savings, substituting high-emission fuels with low-emission or renewable energy sources, and applying pollutant neutralization technologies to mitigate emissions. The proposed comprehensive assessment is intended for the selection of effective technologies for emission reduction. The methodology of the comprehensive assessment includes environmental indicators to ensure compliance with emission limits according to the accepted obligations, economic indicators to evaluate costs related to fuel savings, purification equipment, and emission reduction measures, and energy indicators to assess fuel consumption reductions and additional electricity consumption for purification systems. It is proposed to compare possible options for implementing environmental protection measures using the criterion of minimum costs. The originality of the methodological approach lies in its universality, the possibility of application for assessing measures at various hierarchical levels of the economy: country, type of economic activity, installation. Keywords: emissions of pollutants, types of economic activity, energy saving, assessment of environmental protection measures.