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1. Enhancing Probabilistic Solar PV Forecasting: Integrating the NB-DST Method with Deterministic Models

   https://doi.org/10.3390/en17102392  

   Ahmad, Tawsif; Zhou, Ning; Zhang, Ziang; Tang, Wenyuan (May 2024, Energies)

   Barambones, Oscar (Ed.)

   Accurate quantification of uncertainty in solar photovoltaic (PV) generation forecasts is imperative for the efficient and reliable operation of the power grid. In this paper, a data-driven non-parametric probabilistic method based on the Naïve Bayes (NB) classification algorithm and Dempster–Shafer theory (DST) of evidence is proposed for day-ahead probabilistic PV power forecasting. This NB-DST method extends traditional deterministic solar PV forecasting methods by quantifying the uncertainty of their forecasts by estimating the cumulative distribution functions (CDFs) of their forecast errors and forecast variables. The statistical performance of this method is compared with the analog ensemble method and the persistence ensemble method under three different weather conditions using real-world data. The study results reveal that the proposed NB-DST method coupled with an artificial neural network model outperforms the other methods in that its estimated CDFs have lower spread, higher reliability, and sharper probabilistic forecasts with better accuracy. 

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2. Exploiting Satellite Data for Solar Performance Modeling

   https://doi.org/10.1109/SmartGridComm47815.2020.9302984  

   Bansal, Akansha Singh; Irwin, David (November 2020, 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm))

   null (Ed.)

   Developing accurate solar performance models, which infer solar power output in real time based on the current environmental conditions, are an important prerequisite for many advanced energy analytics. Recent work has developed sophisticated data-driven techniques that generate customized models for complex rooftop solar sites by combining well-known physical models with both system and public weather station data. However, inferring solar generation from public weather station data has two drawbacks: not all solar sites are near a public weather station, and public weather data generally quantifies cloud cover-the most significant weather metric that affects solar-using highly coarse and imprecise measurements.In this paper, we develop and evaluate solar performance models that use satellite-based estimates of downward shortwave (solar) radiation (DSR) at the Earth's surface, which NOAA began publicly releasing after the launch of the GOES-R geostationary satellites in 2017. Unlike public weather data, DSR estimates are available for every 0.5km 2 area. As we show, the accuracy of solar performance modeling using satellite data and public weather station data depends on the cloud conditions, with DSR-based modeling being more accurate under clear skies and station-based modeling being more accurate under overcast skies. Surprisingly, our results show that, overall, pure satellite-based modeling yields similar accuracy as pure station-based modeling, although the relationship is a function of conditions and the local climate. We also show that a hybrid approach that combines the best of both approaches can also modestly improve accuracy. 

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3. Prediction of Solar Irradiance and Photovoltaic Solar Energy Product Based on Cloud Coverage Estimation Using Machine Learning Methods

   https://doi.org/10.3390/atmos12030395  

   Park, Seongha; Kim, Yongho; Ferrier, Nicola J.; Collis, Scott M.; Sankaran, Rajesh; Beckman, Pete H. (March 2021, Atmosphere)

   Cloud cover estimation from images taken by sky-facing cameras can be an important input for analyzing current weather conditions and estimating photovoltaic power generation. The constant change in position, shape, and density of clouds, however, makes the development of a robust computational method for cloud cover estimation challenging. Accurately determining the edge of clouds and hence the separation between clouds and clear sky is difficult and often impossible. Toward determining cloud cover for estimating photovoltaic output, we propose using machine learning methods for cloud segmentation. We compare several methods including a classical regression model, deep learning methods, and boosting methods that combine results from the other machine learning models. To train each of the machine learning models with various sky conditions, we supplemented the existing Singapore whole sky imaging segmentation database with hazy and overcast images collected by a camera-equipped Waggle sensor node. We found that the U-Net architecture, one of the deep neural networks we utilized, segmented cloud pixels most accurately. However, the accuracy of segmenting cloud pixels did not guarantee high accuracy of estimating solar irradiance. We confirmed that the cloud cover ratio is directly related to solar irradiance. Additionally, we confirmed that solar irradiance and solar power output are closely related; hence, by predicting solar irradiance, we can estimate solar power output. This study demonstrates that sky-facing cameras with machine learning methods can be used to estimate solar power output. This ground-based approach provides an inexpensive way to understand solar irradiance and estimate production from photovoltaic solar facilities. 

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4. Time-Series Feature Selection for Solar Flare Forecasting

   https://doi.org/10.3390/universe10090373  

   Velanki, Yagnashree; Hosseinzadeh, Pouya; Boubrahimi, Soukaina Filali; Hamdi, Shah Muhammad (September 2024, Universe)

   Solar flares are significant occurrences in solar physics, impacting space weather and terrestrial technologies. Accurate classification of solar flares is essential for predicting space weather and minimizing potential disruptions to communication, navigation, and power systems. This study addresses the challenge of selecting the most relevant features from multivariate time-series data, specifically focusing on solar flares. We employ methods such as Mutual Information (MI), Minimum Redundancy Maximum Relevance (mRMR), and Euclidean Distance to identify key features for classification. Recognizing the performance variability of different feature selection techniques, we introduce an ensemble approach to compute feature weights. By combining outputs from multiple methods, our ensemble method provides a more comprehensive understanding of the importance of features. Our results show that the ensemble approach significantly improves classification performance, achieving values 0.15 higher in True Skill Statistic (TSS) values compared to individual feature selection methods. Additionally, our method offers valuable insights into the underlying physical processes of solar flares, leading to more effective space weather forecasting and enhanced mitigation strategies for communication, navigation, and power system disruptions. 

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5. A photothermal solar tunnel via multiple transparent Fe3O4@Cu2-xS thin films for heating utility application

   https://doi.org/10.1016/j.solener.2024.112444  

   Katepalli, Anudeep; Wang, Yuxin; Lin, Jou; Harfmann, Anton; Bonmarin, Mathias; Krupczak, John; Shi, Donglu (March 2024, Solar Energy)

   A Photothermal Solar Tunnel Radiator (PSTR) is designed and developed by employing multiple transparent photothermal glass panels (TPGP). The primary objective is to pioneer a transformative approach to achieve energy-neutral building heating utilities, exemplified by a lab-scale "Photothermal Solar Box" (PSB) exclusively heated with TPGP under natural sunlight. The PSTR presents a novel paradigm for sustainable energy, enabling direct solar energy capture through transparent glass substrates with photothermal coatings. The high transparency of Fe3O4@Cu2-xS coated glass substrates enhance efficient solar harvesting and photothermal energy generation within the Photothermal Solar Box. The system demonstrates an impressive thermal energy output, reaching up to 9.1x105 joules with 8 photothermal panels in parallel. Even under colder conditions (ambient temperature: -10 °C), with accelerated heat loss, the interior temperatures of the PSB with partial thermal insulation achieve a commendable 35 °C, showcasing effective photothermal heating in cold weather. These findings indicate the system's resilience and efficiency in harnessing solar energy under diverse conditions, including partial cloudy weather. The initiative contributes to broader sustainability goals by providing a scalable and practical alternative to traditional solar heating methods, aligning with the global mission for a cleaner, greener future. 

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