The paper evaluates short‑term load forecasting methods that are sensitive to high‑demand periods across three distribution grid aggregation levels—area codes, secondary substations, and low‑voltage feeders—using UK and Swiss datasets. It compares statistical baselines, LightGBM, XGBoost, and foundation models Chronos Bolt and Chronos‑2, finding that Chronos‑2 delivers the best high‑demand performance and remains competitive overall. The study also shows that foundation model inference is fast enough for deployment and that peak‑aware evaluation and aggregation‑specific quantile selection can improve operational relevance.
By Souhardya Chattopadhyay, Julian Oelhaf, Antonia Schoening, Jessica Deuschel, Bitan Bhattacharyya, Christian Bergler, Andreas Maier, Siming Bayer
arXiv:2607. 01966v1 Announce Type: new Abstract: Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation.
By Benedikt Kaas, Manuel Treutlein, Hannes Benedikt Gerber, Oliver Neumann, Cheewan Phatthanakhuha, Oliver Resch, Ralf Mikut, Veit Hagenmeyer
arXiv:2607. 15705v1 Announce Type: new Abstract: Accurate load forecasting at multiple grid levels is essential for future smart grids, ranging from aggregated control area forecasts for balancing supply and demand to forecasts of individual end-consumer loads for demand-side management and energy management systems.
By Matthias Hertel, Sebastian P\"utz, Jonathan Kolar, Benjamin Sch\"afer, Ralf Mikut, Veit Hagenmeyer
Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation. However, current forecasting methods require significant manual effort, often lack uncertainty estimation and proper peak prediction, and they are often not adequately evaluated in terms of grid requirements.
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
arXiv:2609.06656v1 Announce Type: cross
Abstract: Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load foreca...
By Varsha Pendyala, Yiwei Fu, Weizhong Yan, Nurali Virani
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
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
arXiv:2411. 11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios.
By Wenlong Liao, Chengrui Zhang, Zhe Yang, Mengshuo Jia, Christian Rehtanz, Jiannong Fang, Fernando Port\'e-Agel
The paper introduces Generalized Gibbs Ensemble Weighting (GGEW), a probabilistic framework that assigns weights to forecasting models using a Gibbs-style exponential transformation of normalized predictive loss. GGEW extends basic weighting through numerical stabilization, diversity-aware score corrections, and online hyperparameter adaptation, yielding variants such as Stable Gibbs weighting, Directional Gibbs-NCL, and Symmetric Gibbs-NCL. The authors evaluate GGEW on M4 competition submissions and real-world datasets (Monash Traffic, Electricity, Solar), finding that Gibbs-style adaptive weighting is competitive across various settings, though performance varies by dataset, horizon, and deployment protocol.
By Prasen R. Nuthanakaluva, Nava K. Gaddam
arXiv:2607. 19974v1 Announce Type: cross Abstract: The rapid proliferation of data-intensive applications, cloud infrastructure, and IoT ecosystems has made proactive resource provisioning critical for maintaining optimal network performance.
By Niraj Gadhe, Kirti Bhardwaj, Moulik Jain, Shubhi Sharma, Vinay Saini
arXiv:2609.13840v1 Announce Type: new
Abstract: A contract-logistics spare-parts operator is paid on order-level service: an order counts only if every requested line is fulfilled, yet forecasters ar...
By Joo Ern Chin, Shih-Fen Cheng, Aldy Gunawan