The paper presents a Bayesian framework that uses remote sensing to estimate the true rooftop photovoltaic (PV) capacity, correcting for detector imperfections and providing uncertainty estimates. Applied to France, the method yields a 4.03 GWp estimate for rooftop PV below 36 kWp, aligning with transmission operator data within 3.3% nationally but revealing local under‑reports up to 61%. It also identifies a significant truncation bias in French open PV data, highlighting the need for more reliable capacity estimates worldwide.
arXiv:2607.25153v2 Announce Type: replace Abstract: Rooftop photovoltaic (PV) systems account for the vast majority of PV grid connections, yet no open, comprehensive, installation-level dataset of t...
SolarBench is an open global benchmark for image-based solar nowcasting that consolidates over six million sky and satellite images from 11 sites across a decade, paired with irradiance, PV output, and atmospheric data. The benchmark includes a toolbox for reproducible data access, processing, model development, and evaluation. Using SolarBench, the authors benchmark representative models, uncover a gap between average forecasting accuracy and the capture of rapid solar fluctuations, quantify predictability across cloud regimes, and demonstrate data‑efficient adaptation to new PV systems.
The paper introduces a transfer learning framework paired with Conformalized Quantile Regression (CQR) to enhance solar PV power forecasting in regions with limited historical data due to load shedding. Using a source-domain dataset from Alice Springs, Australia, the model is pretrained and then adapted to simulated Bangladesh PV data with varying data availability. Experiments show that transfer learning can reduce RMSE by up to 23.7% with only one month of target data, and the combined approach achieves 94.3% empirical coverage with prediction intervals 14% narrower than without transfer learning.
arXiv:2608. 02088v1 Announce Type: new Abstract: Reliable photovoltaic (PV) forecasts are needed for low-carbon energy systems, but newly deployed sites often have short, imperfect records.
arXiv:2411. 12193v4 Announce Type: replace-cross Abstract: The rapid growth of distributed energy resources (DERs) presents both opportunities and operational challenges for electric grid management.
The paper presents a two-part workflow for analyzing smart‑meter data to uncover patterns of residential co‑adoption of photovoltaic (PV) systems and electric vehicles (EVs). First, dynamic time warping k‑means clustering identifies distinct daily import/export archetypes for PV‑only, EV‑only, co‑adopters, and neither groups, revealing a midday‑centered export pattern for many co‑adopters. Second, a bidirectional LSTM model trained on 21‑day windows achieves high detection performance (AUROC 0.991, macro‑F1 0.906) for PV/EV activity, outperforming tabular baselines and remaining robust across labeling rules and temporal splits.
PROSWIN is a probabilistic machine learning model that forecasts hourly solar wind speed at Earth up to four days ahead, using solar images and magnetograms processed by a deep neural network and distributional regression. It introduces a prediction score metric that rewards both timeline and high‑speed solar wind peak accuracy, achieving well‑calibrated uncertainties and superior performance on 14 years of data compared to existing models. The study highlights the importance of the 171 Å channel and demonstrates that probabilistic forecasts outperform single‑value models for both overall timelines and peak events.
Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs. In photovoltaic (PV) forecasting, this requirement is especially challenging because numerical weather prediction (NWP) errors are temporally correlated, state dependent, and physically coupled across variables.
arXiv:2606. 00506v1 Announce Type: new Abstract: Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning.
arXiv:2607. 12954v1 Announce Type: cross Abstract: Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs.
arXiv:2606. 09517v1 Announce Type: new Abstract: As renewable energy integration increases market volatility, probabilistic electricity price forecasting has become essential for effective risk management.
arXiv:2609.15049v1 Announce Type: cross Abstract: An ensemble can improve photovoltaic forecasts while adding components that contribute little or increase computation. We assess these effects throug...