Impact of Domain Nesting on High-Resolution Forecasts of Solar Conditions in Central and Eastern Europe

Mierzwiak, Michał; Kroszczyński, Krzysztof

doi:10.3390/en16134969

Cited by 3 publications

(1 citation statement)

References 71 publications

(90 reference statements)

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“…The efficacy and staging of solar power systems are strongly influenced by solar irradiance, which is the amount of sunlight received on a specified surface during a specific period. Due to the growing use of electricity from solar energy on the one hand and the growth of this energy integration into the electricity grid on the other hand [1,2], it is becoming increasingly important to predict the amount of this renewable energy source. This prediction must imperatively involve the forecasting of meteorological data such as irradiation and temperature.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

Tercha,

Tadjer,

Chekired

et al. 2024

Energies

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The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

Tercha,

Tadjer,

Chekired

et al. 2024

Energies

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Analyzing the Improvement Effect of the k-Distribution Method on the Radiation Parameterization for WRF Model

Choi,

Jee,

Lee

et al. 2024

Atmosphere

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To address the need for the accurate parameterization of radiative absorption by gasses (for predicting atmospheric warming), Chou et al. developed a new k-distribution method. In this study, we compared the improved k-distribution method (hereinafter referred to as the NEW method) with the New Goddard radiation schemes (hereinafter referred to as the OLD method) for the WRF (the weather research and forecasting) model. The results of radiative flux calculations by the NEW and OLD methods of k-distribution using the New Goddard Radiation Scheme were compared with the results of the line-by-line (LBL) method, and the results showed that the radiative flux calculated by the NEW was accurate to within 1.00 Wm−2 with respect to the LBL, while the OLD showed large differences at altitudes above the upper troposphere and near the surface. Therefore, in this study, we selected clear-sky and cloudy-day conditions and compared the weather elements prediction results of WRF using the NEW and OLD methods. For the clear-sky days, the downward shortwave radiation at the surface and the temperature at 2 m above the surface (hereinafter referred to as T2) over land and ocean were reversed in sign due to the highly sensitive absorption coefficients of gasses. For cloudy days, the absorption effect by gasses harmonized with the scattering effect induced by cloud droplets; the differences in the shortwave and longwave radiations and radiative heating rate between the NEW and OLD methods were obvious. Thus, it was analyzed that the proposed NEW method could lead to significant improvements in forecasting weather elements.

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A Comparative Study of Machine Learning Models for Predicting Meteorological Data in Agricultural Applications

Šuljug,

Spišić,

Grgić

et al. 2024

Electronics

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This study aims to address the challenges of climate change, which has led to extreme temperature events and reduced rainfall, using Internet of Things (IoT) technologies. Specifically, we monitored the effects of drought on maize crops in the Republic of Croatia. Our research involved analyzing an extensive dataset of 139,965 points of weather data collected during the summer of 2022 in different areas with 18 commercial sensor nodes using the Long-Range Wide Area Network (LoRaWAN) protocol. The measured parameters include temperature, humidity, solar irradiation, and air pressure. Newly developed maize-specific predictive models were created, taking into account the impact of urbanization on the agrometeorological parameters. We also categorized the data into urban, suburban, and rural segments to fill gaps in the existing literature. Our approach involved using 19 different regression models to analyze the data, resulting in four regional models per parameter and four general models that apply to all areas. This comprehensive analysis allowed us to select the most effective models for each area, improving the accuracy of our predictions of agrometeorological parameters and helping to optimize maize yields as weather patterns change. Our research contributes to the integration of machine learning and AI into the Internet of Things for agriculture and provides innovative solutions for predictive analytics in crop production. By focusing on solar irradiation in addition to traditional weather parameters and accounting for geographical differences, our models provide a tool to address the pressing issue of agricultural sustainability in the face of impending climate change. In addition, our results have practical implications for resource management and efficiency improvement in the agricultural sector.

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Impact of Domain Nesting on High-Resolution Forecasts of Solar Conditions in Central and Eastern Europe

Cited by 3 publications

References 71 publications

Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

Analyzing the Improvement Effect of the k-Distribution Method on the Radiation Parameterization for WRF Model

A Comparative Study of Machine Learning Models for Predicting Meteorological Data in Agricultural Applications

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