The paper presents a hierarchical ensemble for short‑term photovoltaic power forecasting that fuses temporal neural models, historical analogs, state climatology, and gradient‑boosted trees with horizon‑specific convex weights. Solar geometry and numerical weather forecasts provide expected generation conditions, while a calibration step corrects recent bias using historical forecast errors. Evaluated on PVDAQ and GEFCom2014 data, the ensemble reduces mean absolute error by up to 6% compared to strong individual models, though its advantage varies with dataset and horizon.
By Xu Yuqing, Zhou Liguo, Sun Ze, Yu Lei, Jiang Mingming
The paper investigates whether combining a strong tree-based photovoltaic forecast model with other temporal predictors can lower its mean absolute error (MAE) over 15–240 minute horizons. A weighted ensemble, constrained to nonnegative weights that sum to one, is trained to minimize MAE, allowing unhelpful models to receive zero weight while the tree anchors the combination. On 56,078 windows from two co-located PVDAQ systems, the shared-MAE policy reduces scaled MAE by 3.21% relative to the tree alone, but adding the latest bounded neural correction does not improve performance further.
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.
By Fariba Dehghan, Sebastian Stein, Vahid Yazdanpanah, Stephanie Gauthier, Masood Nazari
arXiv:2610.27763v1 Announce Type: new
Abstract: The rapid growth of distributed photovoltaic (PV) generation is increasing the demand for accurate forecasting across large numbers of geographically d...
By Dayin Chen, Xiaodan Shi, Xin Xu, Zhongyue Su, Shibo Zhu, Ruizhe Deng, Dongxiao Zhang, Yuntian Chen, Jinyue Yan
arXiv:2610.27155v1 Announce Type: new
Abstract: Accurate multi-horizon photovoltaic (PV) power forecasting is essential for grid stability, energy scheduling, and the efficient operation of PVbattery...
By Shafi Bin Sultan, Sabik Bin Sultan, Mohammad Tajrian Arafat Alvi, Rayhanul Islam Sony, Abdulah Al Numan
arXiv:2610.40418v1 Announce Type: cross
Abstract: This work develops a physics-informed multimodal deep learning framework for 10- to 110-minute-ahead probabilistic photovoltaic power forecasting. Th...
By Anushka Katiyar, Cong Feng, Jing Wang, Tristan Liu, Simon Julien, Zachary Jacobs
arXiv:2607. 11470v1 Announce Type: cross Abstract: Reliable uncertainty quantification is essential for integrating solar and wind generation into modern power systems, where operators must weigh risk rather than act on point forecasts alone.
By Shreedhar Gangwar (B. R. Ambedkar National Institute of Technology, Jalandhar, India), Abhinav Bains (B. R. Ambedkar National Institute of Technology, Jalandhar, India), Banalaxmi Brahma (B. R. Ambedkar National Institute of Technology, Jalandhar, India)
arXiv:2606. 07457v1 Announce Type: new Abstract: At commissioning time, Photovoltaic (PV) operators must forecast production before target-site observations are available, limiting the direct use of standard supervised forecasters.
By Lorenzo Longarini, Alessandro Rongoni, Simone Silenzi, Emanuele Frontoni, Riccardo Rosati
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.
By Yuhao Nie, Stephen Campbell, Quentin Paletta, Liwenbo Zhang, Tao Jing, Samer Chaaraoui, Jonathan Giezendanner, Andea Scott, Tao Sun, Cong Feng, Max Aragon, Jacques Camier, Adam Jensen, Florian Kotthoff, Yuexing Yang, Yang Ming, Mengying Li, Stefanie Meilinger, Yupeng Wu, Adam Brandt, Sherrie Wang
The paper investigates how the operational value of deep‑learning PV disaggregation depends on deployment regimes—whether the model serves known customers, new customers, future periods, or new regions. Using Ausgrid smart‑meter data and cross‑dataset adaptation to Pecan Street, the authors benchmark several deep sequence architectures and find that deployment regime explains more error variation than model choice. Fine‑tuning on a small number of target‑region households can significantly reduce transfer error, and the benefits of long input windows vary by use case.
By Jack Zheng, Hao Wang
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: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.
By Dandan Chen, Yan Zhao, Xuepeng Chen