arXiv Machine Learning

Electricity price forecasting across Norway's five bidding zones in the post-crisis era

arXiv:2604. 26634v2 Announce Type: replace Abstract: Norway's electricity market is heavily dominated by hydropower, but the 2021-2022 energy crisis and stronger integration with Continental Europe have fundamentally altered price formation, reducing the reliability of forecasting models calibrated on historical data.

arXiv AI

6d ago

Adaptive Online Learning with LSTM Networks for Energy Price Prediction

arXiv:2510. 16898v2 Announce Type: replace-cross Abstract: Accurate prediction of electricity prices is crucial for stakeholders in the energy market, particularly for grid operators, energy producers, and consumers.

By Salih Salihoglu, Ibrahim Ahmed, Afshin Asadi

safety

arXiv AI

Jul 28

AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS

arXiv:2602. 16579v2 Announce Type: replace-cross Abstract: Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transitioning from historical reanalysis to operational forecast products.

By Maria Luisa Taccari, Kenza Tazi, Ois\'in M. Morrison, Andreas Grafberger, Juan Colonese, Corentin Carton de Wiart, Christel Prudhomme, Cinzia Mazzetti, Matthew Chantry, Florian Pappenberger

fine-tuning benchmarks

arXiv Machine Learning

Jun 17

Delta-Based Target Reformulation for Short-Term Electricity Load Forecasting Using LSTM and Transformer Models

arXiv:2606. 17692v1 Announce Type: new Abstract: Accurate short-term electricity load forecasting is critical for the reliable and economic operation of modern power systems, under non-stationarity arising from weather variability, calendar effects, and evolving consumption patterns.

By Vansh Bansal

llms benchmarks safety

arXiv Machine Learning

Jul 21

Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables

arXiv:2603. 15802v2 Announce Type: replace Abstract: In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load; calendar and holiday indicators for traffic or sales; and grid load or fuel costs in electricity pricing.

By Andres Potapczynski, Ravi Kiran Selvam, Tatiana Konstantinova, Malcolm Wolff, Kin G. Olivares, Ruijun Ma, Michael W. Mahoney, Andrew Gordon Wilson, Boris N. Oreshkin, Dmitry Efimov

benchmarks

arXiv AI

Jun 4

Physics-Informed Machine Learning for Short-Term Flood Prediction

arXiv:2606. 04143v1 Announce Type: cross Abstract: Accurate flood forecasting is essential for mitigating disaster risks and protecting communities.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

safety

arXiv AI

Jun 18

Analysing drivers and interdependencies in European electricity markets using XAI

arXiv:2606. 19118v1 Announce Type: new Abstract: Electricity markets are inherently complex systems characterised by strong nonlinearities, high-dimensional interactions, and increasing interdependence across regions.

By Antoine Pesenti, Aidan O'Sullivan

safety

arXiv AI

Jul 21

Short-Term Electricity Demand Forecasting for New England: A Comprehensive Machine Learning Benchmark with Weather, Calendar, and COVID-19 Indicators

arXiv:2606. 20918v2 Announce Type: replace-cross Abstract: Accurate short-term electricity demand forecasting is critical for reliable power system operation, energy market planning, and infrastructure optimization.

By Reza Ghanavati, Behrooz Mosallaei

llms benchmarks

arXiv AI

Jul 21

FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting

arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.

By Marco Obermeier, Marco Pruckner, Florian Haselbeck, Andreas Zeiselmair

benchmarks

arXiv AI

Aug 6

Short-term load forecasting under EU-AI Act Requirements in Safety-Critical Environments: Results from a 41-day live challenge on the aggregated German transmission-grid load

arXiv:2608. 05018v1 Announce Type: new Abstract: Short-term load forecasting (STLF) play a vital role in the electric power industry.

By Thomas Bartz-Beielstein

benchmarks safety

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 8

Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates

arXiv:2607. 05452v1 Announce Type: new Abstract: Time series forecasters that use exogenous covariates are fragile in deployment: when those covariates are noised, temporally misaligned, or missing, strong exogenous-fusion and exogenous-adapted models can degrade far above the endogenous-only floor.

By Hao Hu, Xue-shan Ai

benchmarks

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