Probabilistic Solar Proxy Forecasting With Neural Network Ensembles by Joshua D. Daniell, Piyush M. Mehta

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Probabilistic forecasting of renewable energy and electricity demand using Graph-based Denoising Diffusion Probabilistic Model by Amir Miraki, Pekka Parviainen, Reza Arghandeh

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Adapting Ensemble‐Calibration Techniques to Probabilistic Solar‐Wind Forecasting by N. O. Edward‐Inatimi, M. J. Owens, L. Barnard, H. Turner, M. Marsh, S. Gonzi, M. Lang, P. Riley

“…Ensemble forecasting generates sets of outputs to create probabilistic forecasts which quantify forecast uncertainty, vital for reliable/actionable forecasts. …”
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Probabilistic Forecasting of Ground Magnetic Perturbation Spikes at Mid‐Latitude Stations by Michael Coughlan, Amy Keesee, Victor Pinto, Raman Mukundan, José Paulo Marchezi, Jeremiah Johnson, Hyunju Connor, Don Hampton

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Probabilistic Short‐Term Solar Driver Forecasting With Neural Network Ensembles by Joshua D. Daniell, Piyush M. Mehta

“…We provide the first probabilistic forecasting method for S10.7, M10.7, and Y10.7. …”
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Quantile Regression for Probabilistic Electricity Price Forecasting in the U.K. Electricity Market by Yuki Osone, Daisuke Kodaira

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Probabilistic Forecasts of Storm Sudden Commencements From Interplanetary Shocks Using Machine Learning by A. W. Smith, I. J. Rae, C. Forsyth, D. M. Oliveira, M. P. Freeman, D. R. Jackson

“…The models also produce skillful forecasts of SSCs, though with less reliability than was found for SCs. …”
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Decomposition-Aware Framework for Probabilistic and Flexible Time Series Forecasting in Aerospace Electronic Systems by Yuanhong Mao, Xin Hu, Yulang Xu, Yilin Zhang, Yunan Li, Zixiang Lu, Qiguang Miao

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Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting by Zhuofu Deng, Binbin Wang, Heng Guo, Chengwei Chai, Yanze Wang, Zhiliang Zhu

“…Due to the difficulty in producing reliable point forecasts, probabilistic load forecasting becomes more popular as a result of catching the volatility and uncertainty by intervals, density, or quantiles. …”
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Probabilistic Forecasting of Crude Oil Prices Using Conditional Generative Adversarial Network Model with Lévy Process by Mohammed Alruqimi, Luca Di Persio

“…By employing conditional supervised training, the inherent structure of the data distribution is preserved, thereby enabling more accurate and reliable probabilistic price forecasts. Additionally, the CO-CGAN integrates a Lévy process and sentiment features to better account for uncertainties and price shocks in the crude oil market. …”
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Probabilistic forecasting of multiple plant day-ahead renewable power generation sequences with data privacy preserving by Hong Liu, Zijun Zhang

Subjects: “…Probabilistic forecast…”
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A Comprehensive Survey of Electric Vehicle Charging Demand Forecasting Techniques by Mamunur Rashid, Tarek Elfouly, Nan Chen

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Effect of the Ensemble System Size on the Precipitation Forecast Accuracy by Atefeh Mohamadi, Majid Azadi

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Ultra-Short-Term Distributed Photovoltaic Power Probabilistic Forecasting Method Based on Federated Learning and Joint Probability Distribution Modeling by Yubo Wang, Chao Huo, Fei Xu, Libin Zheng, Ling Hao

“…The accurate probabilistic forecasting of ultra-short-term power generation from distributed photovoltaic (DPV) systems is of great significance for optimizing electricity markets and managing energy on the user side. …”
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Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation by Aamir Ali, Sumbal Aslam, Sohrab Mirsaeidi, Noor Hussain Mugheri, Riaz Hussain Memon, Ghulam Abbas, Hammad Alnuman

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Forecasting GOES 15 >2 MeV Electron Fluxes From Solar Wind Data and Geomagnetic Indices by C. Forsyth, C. E. J. Watt, M. K. Mooney, I. J. Rae, S. D. Walton, R. B. Horne

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Uncertainty Quantification for Machine Learning‐Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting by Randa Natras, Benedikt Soja, Michael Schmidt

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Relativistic Electron Flux Prediction at Geosynchronous Orbit Based on the Neural Network and the Quantile Regression Method by Hui Zhang, Suiyan Fu, Lun Xie, Duo Zhao, Chao Yue, Zuyin Pu, Ying Xiong, Tong Wu, Shaojie Zhao, Yixin Sun, Bo Cui, Zhekai Luo

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Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models by Richard J. Licata, Piyush M. Mehta

“…The newly developed reduced order probabilistic emulator (ROPE) uses Long‐Short Term Memory neural networks to perform time‐series forecasting in the reduced state and provide distributions for future density. …”
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Evaluating Auroral Forecasts Against Satellite Observations by M. K. Mooney, M. S. Marsh, C. Forsyth, M. Sharpe, T. Hughes, S. Bingham, D. R. Jackson, I. J. Rae, G. Chisham

“…The model performance is reduced in the dayside local time sectors (ROC score = 0.59) and during periods of higher geomagnetic activity (ROC score of 0.55 for Kp = 8). As a probabilistic forecast, OVATION‐Prime 2013 tends to underpredict the occurrence of aurora by a factor of 1.1–6, while probabilities of over 90% are overpredicted.…”
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