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.
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.
As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy forecasting for efficient operations and planning. Generally, observed weather data are applied in the solar PV generation forecasting model w…
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.
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.
Electricity production via solar energy is tackled via short-term forecasts and risk management. Our main tool is a new setting on time series. It allows the definition of "confidence bands" where the Gaussian assumption, which is not satisfied by our concrete data, may be abandoned. Those bands are quite convenient an…
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 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.
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 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.
The paper analyzes MENA region's energy consumption and policy needs for renewable energy.
problem High dependency on oil and low renewable energy penetration in MENA region.
method Analysis of World Bank datasets and policy portfolio in MENA countries.
result MENA region has high potential for solar energy but faces challenges in decoupling economic growth from energy consumption.
Nowadays, with the unprecedented penetration of renewable distributed energy resources (DERs), the necessity of an efficient energy forecasting model is more demanding than before. Generally, forecasting models are trained using observed weather data while the trained models are applied for energy forecasting using for…
Photovoltaic systems have been widely deployed in recent times to meet the increased electricity demand as an environmental-friendly energy source. The major challenge for integrating photovoltaic systems in power systems is the unpredictability of the solar power generated. In this paper, we analyze the impact of havi…
Federated learning calibrates insurance indices from renewable energy producers' data.
problem Calibrating parametric insurance indices under heterogeneous renewable energy production losses.
method Federated learning framework using Tweedie GLMs and distributed optimization.
result Federated learning recovers comparable index coefficients under moderate heterogeneity.
Non-availability of reliable and sustainable electric power is a major problem in the developing world. Renewable energy sources like solar are not very lucrative in the current stage due to various uncertainties like weather, storage, land use among others. There also exists various other issues like mis-commitment of…
The increasing importance of renewable energy, especially solar and wind power, has led to new forces in the formation of electricity prices. Hence, this paper introduces an econometric model for the hourly time series of electricity prices of the European Power Exchange (EPEX) which incorporates specific features like…
Study builds dataset and benchmarks ML models for accurate solar and wind power forecasting in France.
problem Accurate prediction of non-dispatchable renewable energy sources for grid stability and price prediction.
method Comprehensive methodology using machine learning models trained with spatially explicit weather data and production site capacity.
result Neural networks outperform traditional models in forecasting solar and wind power production in France.
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.
A new method uses deep learning to predict full conditional distributions.
problem Lack of uncertainty information in conditional distribution predictions.
method Transformed distribution estimation into multi-class classification, using deep neural networks and a joint binary cross-entropy loss function.
result Improved accuracy in probabilistic solar energy forecasting.
With the increasing penetration of solar power into power systems, forecasting becomes critical in power system operations. In this paper, an hourly-similarity (HS) based method is developed for 1-hour-ahead (1HA) global horizontal irradiance (GHI) forecasting. This developed method utilizes diurnal patterns, statistic…
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.
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.
This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of…
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.
Paper uses conformal prediction for solar power forecasting in electricity markets.
problem Enhancing participation in electricity markets through accurate day-ahead PV power predictions.
method Combines machine learning for point predictions and conformal prediction for uncertainty quantification.
result CP with k-nearest neighbors and Mondrian binning outperforms linear quantile regressors in predicting PV power.
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.
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.
New model predicts particle precipitation from magnetosphere to ionosphere.
problem Improving prediction of electron particle precipitation from magnetosphere to ionosphere.
method Compilation of new database, use of machine learning (ML) tools, neural network (PrecipNet).
result PrecipNet achieves >50% reduction in errors and better captures dynamic changes.
Machine learning predicts perovskite formability and classifies crystal structures.
problem Predicting and classifying perovskite formability and crystal structures.
method Machine learning, specifically Random Forest, with 5-fold cross-validation.
result 98.57% accuracy in predicting perovskite formability and 90.53% in classifying crystal structures.
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.
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.
New method improves wind and solar energy forecasts by 48 hours.
problem Volatility of wind and solar energy makes accurate forecasts difficult.
method Two-step machine learning approach to calibrate ensemble forecasts.
result Statistical post-processing improves forecast skill by at least 48 hours.
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.
Optimizes renewable energy mix to meet carbon-free targets at lowest cost.
problem Minimizing annual procurement costs while achieving specified carbon-free hourly performance.
method Probabilistic framework with simulation scenarios and probability constraints. Fixed set of renewable generators and load customer.
result Demonstrated that certain renewable energy portfolios can meet carbon-free targets at lower costs compared to others.
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.
Paper introduces a neural framework for accurate energy forecasting.
problem Challenges of forecasting energy demand and supply due to variability of renewable sources and dynamic consumption patterns.
method Integrates Neural ODEs, graph attention, multi-resolution wavelet transformations, and adaptive learning of frequencies.
result Consistently outperforms state-of-the-art baselines in various forecasting metrics across diverse datasets.
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.
Paper tackles intermittent learning for energy-constrained machine learning tasks.
problem Energy-constrained machine learning tasks on intermittently powered systems.
method Developed an algorithm and heuristics for efficient learning under energy constraints.
result Improves energy efficiency by up to 100% and reduces learning examples by up to 50%.
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.
Paper tackles RL for power grid topology optimization.
problem Managing large action spaces in growing power networks.
method Hierarchical multi-agent reinforcement learning (MARL) framework.
result MARL framework outperforms single-agent RL methods.
We show that different rates should be used for borrowing and discount rates, and that the risk-free rate should be used for discounting when assessing and comparing the cost of energy accross diffferent producers and technologies, on the example of photovoltaics. Recent quantitative models using the same rate for borr…
Active learning reduces smart meter data needs for better electric load predictions.
problem Inaccurate and costly electric load predictions due to insufficient data.
method Active learning to collect more informative data subsets.
result Electric load predictions can be made with about half the data using active learning.
Using artificial neural network for the prediction of heat demand has attracted more and more attention. Weather conditions, such as ambient temperature, wind speed and direct solar irradiance, have been identified as key input parameters. In order to further improve the model accuracy, it is of great importance to und…
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.
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.
Deep learning improves weather modeling for electricity load forecasting.
problem Accurate load and renewable energy forecasting requires complex spatio-temporal weather modeling.
method Automated spatio-temporal feature extraction using deep neural networks.
result Deep learning outperforms traditional methods in French national load forecasting.
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.