The primary objective of this study is to evaluate the accuracy of different forecasting models for monthly wind farm electricity production. This study compares the effectiveness of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Support Vector Regression (SVR). This study utilizes data from two wind farms located in Poland—‘Gizałki’ and ‘Łęki Dukielskie’—to exclude the possibility of biased results due to specific characteristics of a single farm and to allow for a more comprehensive comparison of the effectiveness of both time series analysis methods. Model parameterization was optimized through a grid search based on the Mean Absolute Percentage Error (MAPE). The performance of the best models was evaluated using Mean Bias Error (MBE), MAPE, Mean Absolute Error (MAE), and R2Score. For the Gizałki farm, the ARIMA model outperformed SARIMA and SVR, while for the Łęki Dukielskie farm, SARIMA proved to be the most accurate, highlighting the importance of optimizing seasonal parameters. The SVR method demonstrated the lowest effectiveness for both datasets. The results indicate that the ARIMA and SARIMA models are effective for forecasting wind farm energy production. However, their performance is influenced by the specificity of the data and seasonal patterns. The study provides an in-depth analysis of the results and offers suggestions for future research, such as extending the data to include multidimensional time series. Our findings have practical implications for enhancing the accuracy of wind farm energy forecasts, which can significantly improve operational efficiency and planning.

The effective operation of photovoltaic systems depends on many factors and parameters that must be continuously monitored. The factors listed in the article are frequently variable, which makes it very difficult to predict the amount of radiation that will reach photovoltaic panels and can be converted into electricity. Therefore, to optimize the operating point of a photovoltaic power plant, it is necessary to track the changes in these quantities. IoT systems may help in controlling and managing a power plant, storage, and energy flow to the power grid. The results recorded at the hybrid power plant of the Faculty of Electrical Engineering of the Bialystok University of Technology are useful for a comprehensive analysis of the operation of the plant and ways of its optimization. It is shown that implementation of a comprehensive maintenance system may deliver extensive important information regarding the PV plant installation.

The high-efficiency operation of photovoltaic and wind systems is affected by many factors and parameters that should be continuously monitored. Since most of the variable factors are related to weather conditions, they are difficult to predict. Therefore, in order to optimize the operating point of a photovoltaic or wind power plant, it is necessary to observe changes in the subject area. The operation of photovoltaic and wind power plants can complement each other. The results recorded at the hybrid power plant of the Faculty of Electrical Engineering of Bialystok University of Technology are useful for a comprehensive analysis of the power plant operation and the ways to optimize it. This paper presents the influence of environmental factors on the operation of a hybrid photo-voltaic–wind power plant located in the city of Bialystok, Poland. The aim of the study was to present the variable factors on the optimal adjustment of the location of the power plant elements at the stage of its design and selection of the energy management system. The presented measurement data from 2015–2021 allow conclusions to be drawn on the significant impact on the power plant’s operation, taking into account both the average conditions corresponding to the analysed location and the full range of changes in the listed factors.

This paper presents the models developed for the short-term forecasting of energy production by photovoltaic panels. An analysis of a set of weather factors influencing daily energy production is presented. Determining the correlation between the produced direct current (DC) energy and the individual weather parameters allowed the selection of the potentially best explanatory factors, which served as input data for the neural networks. The forecasting models were based on MLP and Elman-type networks. An appropriate selection of structures and learning parameters was carried out, as well as the process of learning the models. The models were built based on different time periods: year-round, semi-annual, and seasonal. The models were developed separately for monocrystalline and amorphous photovoltaic modules. The study compared the models with the predicted and measured insolation energy. In addition, complex forecasting models were developed for the photovoltaic system, which could forecast DC and AC energy simultaneously. The complex models were developed according to the rules of global and local modeling. The forecast errors of the developed models were included. The smallest values of the DC energy forecast errors were achieved for the models designed for summer forecasts. The percentage forecast error was 1.95% using directly measured solar irradiance and 5. 57% using predicted solar irradiance. The complex model for summer forecasted the AC energy with an error of 1.86%.

Intelligent LED lamps are most often used in intelligent lighting installations. They frequently use one of two different ways to obtain white light. The first is the mixing of monochrome lights R, G and B. The second way is to mix the light of the blue diode with the yellow phosphor radiation. Adjusting the luminous flux in RGBW LED lamps causes greater changes in the position of the chromaticity point than in the case of CW WW LED lamps. In the case of big changes, this can lead to a visible change in the perceived color. Full Text: PDF References:https://re.jrc.ec.europa.eu/pvg_tools/en/tools.html [DirectLink]https://dateandtime.info/pl [DirectLink]J. Kusznier and W. Wojtkowski, "Impact of climatic conditions on PV panels operation in a photovoltaic power plant," IEEE, 2019 15th Selected Issues of Electrical Engineering and Electronics (WZEE), Zakopane, Poland, 2019, pp. 1-6 [CrossRef]J. Kusznier and W. Wojtkowski, "Impact of climatic conditions and solar exposure on the aging of PV panels," IEEE, 2019 15th Selected Issues of Electrical Engineering and Electronics (WZEE), Zakopane, Poland, 2019, pp. 1-6 [CrossRef]https://www.weatheronline.pl/weather/ [CrossRef]L.T. Sharpe, A. Jagla, W. Jägle, "A luminous efficiency function, V*(λ), for daylight adaptation", J. Vision 5(11), 948 (2012) [CrossRef]http://www.ee.co.za/article/integrating-sphere-data-sincere.html [DirectLink]Simunovic M. P., "On Seeing Yellow The Case for, and Against, Short-Wavelength Light-Absorbing Intraocular Lenses", Archives of Ophthalmology, vol. 130, no. 7, pp. 919-926, 2012 [CrossRef]I. Fryc, "Important parameters of multichannel spectroradiometer", Przeglad Elektrotechniczny R. 88(4a), pp. 230-231 (2012) [DirectLink]I. Fryc, "Measurement techniques of optical LEDs properties performed with compliance conformity with CIE 127:2007 standard", Przeglad Elektrotechniczny R. 85(11), pp. 317-319 (2009) [CrossRef]J. Kowalska, I. Fryc, "Colour rendition quality of typical fluorescent lamps determined by CIE Colour Fidelity Index and Colour Rendering Index", Przeglad Elektrotechniczny R. 95(7), pp. 94-97 (2019) [CrossRef]M. Gilewski, L. Gryko, A. Zajac, "Digital controlling system to the set of high power LEDs", Proc. of SPIE, 8902, 89021D, 2013 [CrossRef]M. Gilewski, "The ecological hazard of artificial lighting in greenhouses" Photonics Letters of Poland, vol. 11 Issue: 3 Pages: 87-89, 2019 [CrossRef]I. Fryc and T. Dimitrova-Grekow, "An automated system for evaluation of the quality of light sources," 2016 IEEE Lighting Conference of the Visegrad Countries (Lumen V4), Karpacz, 2016, pp. 1-4. [CrossRef]I. Fryc, "The spectral power distribution of LED driven by square shape pulses", Przeglad Elektrotechniczny R. 88(6), pp. 131-133 (2012) [DirectLink]I. Fryc, "LED's spectral power distribution under different condition of operating temperature and driving current", Przeglad Elektrotechniczny R. 86(10), pp. 187-189 (2010) [DirectLink]J. Kusznier, "Changes in the Spectral Power Distribution of Light Sources for Smart Lighting," IEEE, 2018 14th Selected Issues of Electrical Engineering and Electronics (WZEE), Szczecin, Poland, 2018, pp. 1-5 [CrossRef]

The increasing number of wind turbines in sparsely populated areas poses significant challenges to the security of the power system. The need for centralized control of unstable sources such as wind turbines and PV installations makes it significantly more difficult to maintain operational stability. The subject matter of wind power plant research can be divided into three groups: wind prediction and productivity forecasts, optimization of the energy generation process, and the impact of power plants on the system and the environment. The subject matter of this article falls within the scope of research on the first group, namely, the impact of wind on actual power production. This study presents the results of a year-long investigation of the Enercon E48 wind turbine located in northeastern Poland. The influence of wind speed and direction on the actual turbine output was analyzed and compared with the manufacturer’s power curve. The findings indicate that the actual performance of the turbine exhibits greater variability than suggested by catalog data, with local conditions and seasonal effects exerting a significant influence on its efficiency.