Energy Load and Power Forecasting

Neueste Paper

• Probabilistic electrical power demand forecasting with uncertainty quantification

  Mahesh Neupane, Pragya Dhungana, Pradip Khatri, Swechhya Baskota, Hariom Dhungana · 29. September 2026

  The majority of research on electricity consumption forecasting has focused on deterministic approaches, which generate a single point estimate for each time step in the forecasting horizon. However, the increasing penetration of renewable energy sources and the growing complexity of modern smart gr…

• Automated feature engineering, AutoML, and decision-focused learning for improved energy consumption forecasting

  Nasser Alkhulaifi · 29. September 2026

  The rising cost and demand for energy, together with environmental sustainability goals, create major challenges for energy management. Energy Consumption Forecasting (ECF) supports planning by predicting future consumption, but Machine Learning (ML) models for ECF often depend on expert-driven Feat…

• GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting

  Eloi Campagne (CB), Yvenn Amara-Ouali (LMO, CELESTE), Yannig Goude (EDF R\&D), Argyris Kalogeratos (CB, ENS Paris Saclay) · 22. September 2026

  Electricity forecasting often involves spatially related signals observed over regions, substations, and feeders, and Graph Neural Networks (GNNs) provide a natural way to represent these relations. Building a complete GNN forecasting experiment is nonetheless laborious, because graph construction, …

• WPBench: A Comprehensive Benchmark for Wind Power Forecasting

  Yuhan Zhu, Jilin Hu, Xinying Cai, Yingshan Li, Li Ma, Xiangfei Qiu Linsen Li, Kai Zhang, Yao Fu, Weihao Jiang, Bin Yang · 22. September 2026

  Accurate, reliable, and deployable wind power forecasting is critical for power system dispatch, renewable energy integration, and electricity market operations. Progress in this field hinges on the ability to empirically and comprehensively benchmark forecasting methods. Yet existing benchmarks fal…

• Ask for Any Appliance: A Prompt-Programmable Foundation Model for Non-Intrusive Load Monitoring

  Xudong Wang, Jiacheng Cui, Junyu Xue, Tongxin Li, Guoming Tang · 22. September 2026

  Non-intrusive load monitoring (NILM) estimates appliance-level consumption from a whole-home meter, but appliance-specific models and fixed output inventories make coverage costly to extend. We present FM4NILM (Foundation Model for NILM), a single prompt-programmable model that estimates a requested…

• EBL: Efficient Broad Learning for Distributed Adaptive Harmonic Analysis

  Changhong Li, Georgios Floros, Biswajit Basu, Shreejith Shanker · 16. September 2026

  Renewable energy systems and electrified transport have found widespread adoption in recent years. The integration of these non-linear loads, dominated by electric vehicle (EV) charging, however, has introduced severe harmonic distortion into the power grid, impacting the efficiency and lifetime of …

• Distributed JEPA: A Self-Supervised Framework for Energy Forecasting

  Liana Toderean, Tudor Cioara, Vasilis Michalakopoulos, Efstathios Sarantinopoulos, Ionut Anghel, Elissaios Sarmas · 16. September 2026

  Traditional energy forecasting solutions rely on task-specific supervision and energy asset representations, limiting transferability and the ability to capture general temporal dynamics across heterogeneous assets. We address this by proposing a distributed Joint Embedding Predictive Architecture (…

• Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis

  Qi Chen, Jierui Zhu, Florian Shkurti · 11. September 2026

  Despite the empirical success of Diffusion Models (DMs) and Variational Autoencoders (VAEs), their generalization performance remains theoretically underexplored, especially lacking a full consideration of the shared encoder-generator structure. Leveraging recent information-theoretic tools, we prop…

• LoaDiff: Conditional Generation of Electricity Consumption Time Series for Energy Analytics

  Mariia Baranova, Adrien Petralia, Etienne Le Naour, Nathan Etourneau, Guillaume Hofmann, Themis Palpanas · 11. September 2026

  The energy transition is reshaping residential electricity consumption through the increasing adoption of distributed generation, electrified appliances, and demand-response programs. Understanding these evolving behaviors requires access to granular smart-meter data for applications such as load fo…

• Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model

  Varsha Pendyala, Yiwei Fu, Weizhong Yan, Nurali Virani · 9. September 2026

  Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load forecasting challenging. Recent advances in time-series foundation models (TSFMs) resulted in promising performance in zero-shot univariate load forecasting …

• Calendar-SPCA: Interpretable Representation Learning for Multi-Periodic Electricity Consumption Profiles

  Carlos Quesada-Granja, Tony Castillo-Calzadilla, Carlos Rizo-Maestre · 9. September 2026

  Long-term electricity-consumption profiles exhibit several simultaneous periodic structures, including daily, weekly, and annual cycles. This work introduces Calendar-SPCA, a calendar-structured sparse principal component method that incorporates this known multi-periodic geometry directly into low-…

• Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts

  Khivishta Boodhoo, Isaac Triguero, Josh Plumbly, Bruce Nicolson, Nicholas Watson · 9. September 2026

  Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns. Accurate wind turbine power predictions can identify periods of low power that would be ideal for scheduling maintenance. However, the effects of dat…

• Foundation models for electricity price forecasting and battery arbitrage: Can they replace market-specific forecasting models?

  Arkadiusz Lipiecki, Rafa{\l} Weron · 2. September 2026

  Foundation models promise accurate forecasts with little or no task-specific training, but whether they can replace models designed specifically for electricity price forecasting remains unclear. We compare nine variants from five foundation model families, evaluated in zero-shot mode, with two stat…

• Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization

  Jianing Chen, Vajiheh Farhadi, Yan Li, Thomas La Porta · 31. August 2026

  Short-term load forecasting (STLF) provides essential information for numerous applications in modern power systems. However, accurate STLF often relies on fine-grained smart-meter data from distributed users, raising increasing concerns about data privacy. Federated learning (FL) has therefore emer…

• Modeling spatio-temporal locality in multi-step forecasting of geo-referenced time series

  Annunziata D'Aversa, Gianvito Pio, Michelangelo Ceci · 27. August 2026

  Forecasting future measurements from geographically distributed sensors is essential across many domains. However, the spatial distribution of these sensors raises multiple challenges, primarily due to spatial autocorrelation phenomena, that introduce inter-dependencies among nearby locations, that …

• Systematic Evaluation of TabPFN-TS for Zero-Shot Probabilistic Heat Load Forecasting in District Heating Networks

  Ben Spoek, Karim K. Ben Hicham, Kai Derzsi, Philipp Althaus, Alexander Mitsos, Dirk M\"uller · 21. August 2026

  District heating energy hubs require reliable heat load forecasts for efficient operational scheduling. Conventional forecasting workflows train system-specific models on historical data, which can become burdensome when networks change through new consumers, retrofits, or changing operating regimes…

• An Empirical Benchmark of Deep Time-Series Models for Smart Meter Energy Forecasting

  Behnaz Kavoosighafi, Maria Eidenskog, Wiktoria Glad, Katerina Vrotsou · 20. August 2026

  Accurate forecasting of energy consumption is important for the efficient operation of power systems, with direct implications for operational costs, energy management, and system maintenance. Due to the availability of extensive high-resolution consumption data from smart meters, data-driven method…

• Deep Learning for Cross-Border Electricity Price Forecasting: A Comparative Study

  Hadeer Elashhab, Sai Srijan Papineni, Marvin Dorn, Veit Hagenmeyer, Benjamin Schäfer · 19. August 2026

  While publicly available electricity market data presents a valuable resource for forecasting research, the field lacks established benchmark datasets for standardized comparison. As a result, many studies have relied on different datasets and metrics to evaluate methods in isolated settings, making…

• Market-Information-Aware Gated-LoRA of Foundation Models for Transferable Day-Ahead Electricity Price Forecasting

  Hang Fan, Wei Wei, Shengwei Mei · 13. August 2026

  Electricity price forecasting is crucial for market participants but remains difficult because prices are volatile, market-specific, and closely tied to anticipated system conditions. Existing supervised methods depend largely on market-specific historical data, limiting their use in newly establish…

• Adaptive Online Learning with LSTM Networks for Energy Price Prediction

  Salih Salihoglu, Ibrahim Ahmed, Afshin Asadi · 13. August 2026

  Accurate prediction of electricity prices is crucial for stakeholders in the energy market, particularly for grid operators, energy producers, and consumers. This study focuses on developing a predictive model leveraging Long Short-Term Memory (LSTM) networks to forecast day-ahead electricity prices…

• Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series

  Thorsten Hoeser, Felix Bachofer, Claudia Kuenzer · 6. August 2026

  Monitoring of offshore wind energy infrastructure life cycles, especially during the deployment phase, is an important contribution for stakeholders to make informed decisions in a phase of increasing deployment activities. ESA's Sentinel-1 Synthetic Aperture Radar (SAR) mission produces large data …

• 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

  Thomas Bartz-Beielstein · 6. August 2026

  Short-term load forecasting (STLF) play a vital role in the electric power industry. It serves infrastructure that European and German law designate as critical. Determinism, reproducibility, and auditability are engineering requirements rather than optional extras. STLF is no longer purely an accur…

• SEDR-Seq2P: A Lightweight Dilated Residual Sequence-to-Point Network for Multi-Task Industrial NILM

  Hatem Haddad, Feres Jerbi, Issam Smaali · 3. August 2026

  Industrial NILM remains challenging because measurement noise and widespread concurrent machine operation reduce the generalization of models tuned on residential data. This work adopts a one-to-many, multi-task disaggregation setting, in which a single network estimates multiple industrial machine …

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

  Hang Ye, Xinyan Jiang, Yuedong Shi, Yangxin Zhu, Jianming Wei, Tian Zheng, Xiaoying Zheng, Yongxin Zhu · 24. Juli 2026

  Modern power systems increasingly require probabilistic forecasts amid interacting uncertainties from renewable intermittency, flexible demand, market volatility, and weather-dependent generation. However, existing methods often treat multi-scale decomposition, exogenous-variable alignment, and prob…

• Domain-Adapted Power Curve for Cross-Farm Applications

  Ahmadreza Chokhachian, V. Roshan Joseph, Yu Ding · 23. Juli 2026

  The wind energy industry relies on accurate power curve models to make power forecast, evaluate turbine performance, quantify upgrade, or support site-planning decisions. In this paper, we focus on site-planning power curves, i.e., we investigate how power curve models trained using turbine data on …