Modeling solar ramping events with spatio-temporal point processes.
problem Predicting solar ramping events influenced by weather conditions.
method Novel spatio-temporal categorical point process model.
result Effective modeling of spatio-temporal correlations in solar ramping events.
Study evaluates post-processing methods for improving solar power forecasts.
problem Improving accuracy of probabilistic solar energy forecasts through model chain approaches.
method Systematically evaluates different post-processing strategies for ensemble weather forecasts and direct solar power forecasting.
result Post-processing significantly improves solar power generation forecasts, especially when applied to power predictions.
Two-stage method improves solar forecasting accuracy.
problem Inaccurate solar forecasting due to nonstationary data.
method Two-stage approach: linear and nonlinear forecasting, data processing.
result Improved accuracy of solar forecasting results.
The study compares statistical post-processing methods for solar radiation forecasts.
problem Improving accuracy and uncertainty quantification of solar radiation forecasts.
method Statistical post-processing techniques using relationships between meteorological variables and solar radiation.
result Quantile regression and generalized random forests generally perform best in probabilistic forecasts.
Paper analyzes how weather data improves solar power prediction.
problem Predicting the unpredictability of solar power generation.
method Examined the impact of weather data on photovoltaic power prediction.
result Weather data significantly improves photovoltaic power prediction accuracy.
Study on weather forecasting errors for solar energy forecasting.
problem Uncertainty in weather forecasting for solar PV generation.
method Comparison of forecasted and observed weather data, statistical metrics, and sensitivity test.
result Identified influential weather variables improving solar PV generation forecasting.
Solar algorithm selects variables faster and more accurately in high-dimensional data.
problem Variable selection in high-dimensional data with high accuracy and stability.
method Subsample-ordered least-angle regression (solar) and its coordinate descent generalization (solar-cd) using L0 norm solution path averaging. result Solar selects variables with high accuracy and stability, reducing redundant variable selection.
A model predicts solar irradiance without local data using satellite and weather forecasts.
problem Forecasting solar irradiance without local measurements for geographically dispersed solar generators.
method Uses satellite data and weather forecasts with a deep neural network trained on a subset of ground data.
result Proposed model performs as well or better than local models across 25 locations and prediction horizons.
Paper proposes deep learning for fault prediction and generator selection in solar power grids.
problem Uncertainty and inefficiencies in solar power systems.
method Deep learning-based system for fault prediction and generator selection.
result Encouraging results suggest potential for application in developing world.
The study finds solar terms significantly impact China's stock market returns and volatility.
problem Investigating the effect of solar terms on China's stock market.
method Regression framework, analyzing multiple solar terms and their impact on return and volatility.
result Solar terms 1, 3, and 4 cause significant positive returns, while 8, 11, and 14 bring high volatility.
DA improves solar wind forecasts by updating model boundary conditions.
problem Improving solar wind forecasting accuracy.
method Variational Data Assimilation with solar wind model and in-situ observations.
result DA forecasts are more accurate than non-DA forecasts, especially when STEREO-B's latitude is offset from Earth.
Study optimizes SREC generation and trading in solar energy markets.
problem Optimizing solar energy generation and trading in SREC markets.
method Mean-field game approach to solve stochastic game with heterogeneous agents.
result Characterized firms' optimal controls and equilibrium SREC price.
Grouped Gaussian Processes improve solar power and wind speed forecasting.
problem Forecasting distributed solar power and wind speed at multiple sites.
method Coupled Gaussian process priors over groups of node and weight functions.
result Our approach maintains or improves point-prediction accuracy and provides better quantification of predictive uncertainties.
Improved probabilistic solar irradiance forecasting models for grid integration.
problem Enhancing accuracy of solar irradiance forecasts for grid integration.
method Developed and calibrated probabilistic models using post-hoc calibration techniques.
result NGBoost model with CRUDE calibration achieves comparable performance to numerical weather prediction models.
The study improves heat demand prediction using neural networks and weather parameters.
problem Improving accuracy of heat demand prediction in district heating networks.
method Used an Elman neural network to investigate the impact of direct solar irradiance and wind speed on heat demand prediction.
result Including wind speed generally results in lower MAPE than including direct solar irradiance, but direct solar irradiance achieves lower maximum absolute deviation.
CGAE model predicts solar irradiance with high reliability and sharpness.
problem Probabilistic spatio-temporal solar irradiance forecasting.
method Convolutional Graph Auto-Encoder (CGAE) based on spectral graph convolutions and variational Bayesian inference.
result State-of-the-art performance in probabilistic solar irradiance prediction.
Paper uses polar field data to improve solar flare prediction accuracy.
problem Improving solar flare prediction accuracy using machine learning.
method Incorporates polar field data into machine learning models for solar flare classification.
result Improves solar flare prediction performance by up to 10.1% using a novel probabilistic mixture of experts model.
Python tool creates machine-learning-ready solar dataset.
problem Creating a usable dataset for space weather forecasting.
method Python tool generates dataset from SoHO and SDO images, applying pre-processing.
result Dataset is machine-learning ready, free of missing data, and temporally synced.
Study evaluates deep learning models for solar flare prediction with interpretability analysis.
problem Lack of interpretability in deep learning models for solar flare prediction.
method Proximity-based metric for analyzing attribution maps generated by Guided Grad-CAM.
result Models' predictions align with active region characteristics, offering insights into their behavior.
Unified architecture for multi-time-scale solar irradiance forecasting reduces RMSE.
problem Non-stationary solar irradiance variability increases grid operating costs.
method Recurrent Neural Networks (RNN) and Long-Short-Term Memory Networks (LSTMs) for multi-time-scale predictions.
result Unified architecture reduces RMSE by 71.5% compared to best-performing methods.
Optimal solar energy production and trading strategies in SREC markets identified.
problem Optimizing solar energy production and trading in SREC markets.
method Formulated a stochastic control problem, accounting for generation and trading costs, and developed a numerical algorithm to solve the control problem.
result An optimal firm's generation and trading behavior can be separated into various regimes based on the marginal benefit of obtaining an additional SREC.
New method for solar energy forecasts and risk management.
problem Short-term forecasting and risk management for solar energy production.
method A new time series setting to define confidence bands, abandoning Gaussian assumptions.
result Easily implementable confidence bands for solar energy production forecasts.
Solar improves variable selection in high-dimensional data with complicated dependence structures.
problem Variable selection in ultrahigh dimensional data with severe multicollinearity and grouping effect issues.
method Subsample-ordered least angle regression (Solar) for ultrahigh dimensional data.
result Solar yields substantial improvements in sparsity, stability, and accuracy of variable selection compared to traditional methods.
Enhances solar flare prediction with advanced preprocessing and contrastive learning.
problem Accurate prediction of solar flares to mitigate risks to astronauts and equipment.
method Advanced data preprocessing pipeline and contrastive learning with GRU regression model.
result Exceptional True Skill Statistic (TSS) scores, surpassing previous methods.
Bayesian framework for solar magnetogram super-resolution with uncertainty quantification.
problem Uncertainty in super-resolving solar magnetic field images.
method Bayesian decomposition of uncertainties into epistemic and aleatoric.
result Generation of maps measuring the range of possible high-resolution explanations.
Deep learning models predict solar irradiance for short-term forecasts.
problem Accurate prediction of solar irradiance for renewable energy integration.
method Sequence-to-sequence LSTM models for GHI forecasting, incorporating spatial-temporal features.
result LSTM models outperform traditional techniques in short-term GHI forecasting.
Unified RNN architecture improves multi-time-horizon solar forecasting.
problem Large prediction errors in traditional solar forecasting methods.
method Recurrent Neural Network (RNN) for multi-time-horizon solar forecasting.
result Lower root-mean-squared prediction error compared to previous methods.
New solar tracking system uses computer vision and low-cost hardware.
problem Current solar tracking systems are expensive and have operational issues.
method Low-cost hardware, computer vision, and deep learning.
result The new system shows great potential and can improve solar tracking performance.
A new solar forecasting method uses clustering and machine learning to improve accuracy.
problem Improving solar forecasting accuracy through weather conditions.
method Unsupervised clustering, pattern recognition, and multi-model machine learning.
result UC-based models outperform non-UC models by approximately 20%.
Interprets LSTM solar flare predictions using SHARP parameters.
problem Understanding the dynamics of solar flares from LSTM predictions.
method Time-series clustering of SHARP parameters to interpret LSTM model.
result Identifies key SHARP parameters for strong solar flares.
Enhances solar cell efficiency prediction using deep neural networks.
problem Predicting HOMO values for organic solar cells from limited experimental data.
method Ensemble deep neural network (SINet) using SMILES and InChI molecular representations.
result Significant performance improvement from transfer learning and dual molecular representations.
Improved solar forecasting using hourly-similarity based method.
problem Critical need for accurate hourly solar forecasting in power systems.
method Developed an HS-based method using diurnal patterns and statistical distinctions.
result HS-based method significantly outperforms non-HS method in forecasting accuracy.
The study improves solar irradiance forecasts for Chile using machine learning.
problem Accurate short-term PV power forecasts for Chile's Atacama Desert.
method 8-member ensemble forecasts of solar irradiance using WRF model, calibrated with EMOS and DRN.
result Machine learning-based post-processing methods improve forecast accuracy and calibration.
Bayesian GAN generates diverse renewable scenarios efficiently.
problem Generating diverse and accurate renewable energy scenarios.
method Bayesian GAN, a deep neural network approach.
result Generates clusters of wind and solar scenarios with different variance and mean values.
Unified NICEk metrics improve solar forecasting accuracy.
problem Lack of suitable error metrics for multidimensional solar irradiance forecasting.
method Introducing NICEk framework with Lk norms for evaluating forecasting models.
result NICESigma consistently outperforms traditional metrics in discriminative power and statistical significance.
GPRNs accurately model stellar activity affecting RV measurements of exoplanets.
problem Stellar activity limits detection and characterisation of exoplanets.
method Gaussian Process Regression Networks (GPRNs) for joint analysis of RV data and stellar activity indicators.
result GPRNs accurately describe solar RV data, correlating with activity at separations of a few days.
Variable renewables can avoid market value decline with policy changes.
problem Market value decline due to correlated generation from wind and solar.
method Theoretical analysis and simulation examples of market incentives and prices.
result Market value decline is due to policy assumptions, not inherent technology limitations.
Study uses machine learning to analyze solar emissions.
problem Explaining the high temperature of the solar corona.
method Unsupervised machine learning to characterize impulsive emissions.
result Characterized over 34,500 features as 2D elliptical Gaussians.
Study assesses forecasting models in uncertain weather data.
problem Performance of forecasting models in uncertain weather data.
method Selected influential predictors, analyzed uncertainty, trained model using observed data.
result Comparison of forecasting methods in solar PV generation.
Generative model connects physical properties to latent vectors for solar magnetic patches.
problem Disconnection between generative latent vectors and scientifically relevant quantities.
method Integrating GAN, SVM, and SSL to generate and retrieve physically interpretable solar magnetic patches.
result GAN-SVM combination enables smooth changes in physical parameters with generated patches.
SolarisNet predicts solar radiation with deep learning, outperforming existing models.
problem Weather turbulence and subjective GSR measurements make accurate solar radiation prediction challenging.
method 6-layer deep neural network trained on Indian weather station data.
result SolarisNet outperforms existing models in GSR prediction and short-term forecasting.
The study predicts solar flare productivity using magnetic data from SDO/HMI.
problem Forecasting solar flares, especially M- and X-class, to mitigate space weather effects.
method Statistical and machine learning methods applied to 563 ARs' magnetic data.
result Improved accuracy in predicting AR's Flare Index, especially for large values.
Quantum kernel improves solar irradiance forecasting.
problem Improving short-term solar irradiance forecasting accuracy.
method Quantum Fourier Transform kernel in KRR with feature mixing.
result Consistently improves R2 and nRMSE over classical kernels.
Modeling wildfire aerosols using satellite data to predict solar radiation reduction.
problem Accurately estimate and predict AOD propagation from wildfires using multi-source satellite data.
method Physics-informed statistical modeling integrating multi-source satellite data with an advection-diffusion equation.
result The proposed approach accurately predicts AOD propagation and demonstrates model interpretability.
Deep learning improves solar energy forecasting using physical and data-driven models.
problem Improving short-term solar energy forecasting accuracy.
method Injecting physical knowledge into deep learning models for spatio-temporal forecasting.
result Improved solar energy forecasting models using deep learning and physical criteria.
Predicts solar dynamics with diffusion models, improving long-range dependencies.
problem Predicting solar dynamics with limited observable data.
method Multiscale inference scheme for diffusion models.
result Improved long-range predictions with reduced bias.
Optimizes solar panel installation to maximize profits from electricity sales.
problem Maximizing profits from selling electricity while considering price impact of installed solar panels.
method Singular stochastic control problem, guess-and-verify approach, ODE solution.
result Optimal installation strategy depends on current installed power and follows a unique curve.
One considers a special class of PDEs systems and one determines the associated symmetry group. Particulary, for the Blair system, one finds the symmetry group. A solutions of the Blair system gives a conformally flat contact metric structure and also it defines a "force-free" model of solar physics. By using the symme…