arXiv Machine Learning

Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction

arXiv:2510. 15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations.

arXiv Machine Learning

Aug 4

An AI-Based Decision-Support Pipeline for Day-Ahead Photovoltaic Forecasting

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

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Hugging Face Trending Papers

Jul 14

Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable Analysis

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 Machine Learning

Jul 15

Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable Analysis

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

arXiv Machine Learning

Jul 31

Bridging AI and Energy Forecasting: An Autonomous Workflow with Customized Toolkit

arXiv:2307. 07191v3 Announce Type: replace Abstract: Energy forecasting is crucial for the power grid, but fundamentally different from general time series analysis: it highly relies on covariates like meteorological factors, and its goals must align with actual power grid operations, such as risk assessment and system reliability.

By Zhixian Wang, Leandro Von Krannichfeldt, Qingsong Wen, Chaoli Zhang, Liang Sun, Shirui Pan, Yi Wang

llms agents benchmarks

arXiv Machine Learning

23h ago

Dynamic Regime-Aware Conformal Calibration for Reliable Economic Forecast Intervals under Multiple Distribution Shifts

arXiv:2608. 17079v1 Announce Type: new Abstract: Conformal prediction provides distribution-free prediction intervals but relies on exchangeability, an assumption often violated in economic forecasting because of covariate shift, concept drift, local heterogeneity and latent regimes.

By Bogdan Oancea

benchmarks

arXiv Machine Learning

Jul 24

SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting

arXiv:2607. 20587v1 Announce Type: cross Abstract: Modern power systems increasingly require probabilistic forecasts amid interacting uncertainties from renewable intermittency, flexible demand, market volatility, and weather-dependent generation.

By Hang Ye, Xinyan Jiang, Yuedong Shi, Yangxin Zhu, Jianming Wei, Tian Zheng, Xiaoying Zheng, Yongxin Zhu

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arXiv AI

Jul 10

PARA-PV: Physics-Aware Retrieval-Augmented PV Prediction Based on Frozen Foundation Model and Distribution Shift Correction

arXiv:2607. 08079v1 Announce Type: new Abstract: Accurate photovoltaic (PV) power forecasting is essential for reliable grid dispatch and renewable energy integration, yet it remains challenging because PV generation is jointly shaped by weather variability, day-night transitions, regime-dependent dynamics, and strict physical constraints.

By Hang Fan, Weican Liu, Ying Lu, Dunnan Liu, Long Cheng, Wei Wei

rag

arXiv Machine Learning

Jun 15

Trend-Aware Multi-Task Learning for Short-Term Energy Forecasting

arXiv:2511. 09789v3 Announce Type: replace Abstract: Short-term energy forecasting plays an important role in real-time operational decision-making, such as electricity market bidding and power system dispatch, where both numerical accuracy and correct directional signals are essential.

By Fulong Yao, Wanqing Zhao, Chao Zheng, Xiaofei Han

benchmarks

arXiv Machine Learning

Jul 1

Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Models

arXiv:2606. 31804v1 Announce Type: new Abstract: Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems.

By Keivan Faghih Niresi, Alice Cicirello, Olga Fink

llms benchmarks safety

arXiv Machine Learning

Jul 14

Climate-Invariant Conformal Prediction Intervals for Multi-Horizon Solar and Wind Forecasting

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 AI

Aug 12

Retrieval-Corrected Conformal Prediction for Time Series

arXiv:2608. 10553v1 Announce Type: cross Abstract: Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters, but its standard calibration procedure is often inefficient for time series data, where forecast errors are temporally dependent and change across time and operating conditions.

By Sangjin Jin, Kangmin Kim, Junhyeong Lee, Yongjae Lee

rag benchmarks

arXiv Machine Learning

Jun 9

Investigating Calibration Challenges in Probabilistic Electricity Price Forecasting

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.

By Jan Niklas Lettner, Hadeer El Ashhab, Benjamin Sch\"afer

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