Papers Load Forecasting
“Load Forecasting” 태그가 달린 논문 235편 · 필터 해제
MetaEformer: Unveiling and Leveraging Meta-patterns for Complex and Dynamic Systems Load Forecasting
Time series forecasting is a critical and practical problem in many real-world applications, especially for industrial scenarios, where load forecasting underpins the intelligent operation of modern systems like clouds, …
Load ForecastingTime Series ForecastingVariational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data
With the development of smart grids, High-Dimensional and Incomplete (HDI) Power Load Monitoring (PLM) data challenges the performance of Power Load Forecasting (PLF) models. In this paper, we propose a potential charact…
DecoderLoad ForecastingManagementShort-Term Power Demand Forecasting for Diverse Consumer Types to Enhance Grid Planning and Synchronisation
Ensuring grid stability in the transition to renewable energy sources requires accurate power demand forecasting. This study addresses the need for precise forecasting by differentiating among industrial, commercial, and…
Demand ForecastingEarth Observationfeature selectionLoad ForecastingLarge Language Model-Empowered Interactive Load Forecasting
The growing complexity of power systems has made accurate load forecasting more important than ever. An increasing number of advanced load forecasting methods have been developed. However, the static design of current me…
Language ModelingLanguage ModellingLarge Language ModelLoad Forecasting+2IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting
Accurate electricity load forecasting is essential for grid stability, resource optimization, and renewable energy integration. While transformer-based deep learning models like TimeGPT have gained traction in time-serie…
Computational EfficiencyDeep LearningDimensionality ReductionFeature Engineering+3Load Forecasting in the Era of Smart Grids: Opportunities and Advanced Machine Learning Models
Electric energy is difficult to store, requiring stricter control over its generation, transmission, and distribution. A persistent challenge in power systems is maintaining real-time equilibrium between electricity dema…
Load ForecastingDiffusion-assisted Model Predictive Control Optimization for Power System Real-Time Operation
This paper presents a modified model predictive control (MPC) framework for real-time power system operation. The framework incorporates a diffusion model tailored for time series generation to enhance the accuracy of th…
Load ForecastingModel Predictive ControlTime SeriesTime Series GenerationMixer-Informer-Based Two-Stage Transfer Learning for Long-Sequence Load Forecasting in Newly Constructed Electric Vehicle Charging Stations
The rapid rise in electric vehicle (EV) adoption demands precise charging station load forecasting, challenged by long-sequence temporal dependencies and limited data in new facilities. This study proposes MIK-TST, a nov…
Kolmogorov-Arnold NetworksLoad ForecastingTransfer LearningDual-Splitting Conformal Prediction for Multi-Step Time Series Forecasting
Time series forecasting is crucial for applications like resource scheduling and risk management, where multi-step predictions provide a comprehensive view of future trends. Uncertainty Quantification (UQ) is a mainstrea…
Conformal PredictionLoad ForecastingManagementScheduling+3Probabilistic Net Load Forecasting for High-Penetration RES Grids Utilizing Enhanced Conditional Diffusion Model
The proliferation of intermittent distributed renewable energy sources (RES) in modern power systems has fundamentally compromised the reliability and accuracy of deterministic net load forecasting. Generative models, pa…
Density EstimationImputationLoad ForecastingUncertainty QuantificationShort-Term Load Forecasting for AI-Data Center
Recent research shows large-scale AI-centric data centers could experience rapid fluctuations in power demand due to varying computation loads, such as sudden spikes from inference or interruption of training large langu…
GPULoad ForecastingDiffusion Model Based Probabilistic Day-ahead Load Forecasting
Accurate probabilistic load forecasting is crucial for maintaining the safety and stability of power systems. However, the mainstream approach, multi-step prediction, must be improved by cumulative errors and latency iss…
DenoisingDensity EstimationLoad ForecastingAn Ensemble Framework for Probabilistic Short-Term Load Forecasting Based on BiTCN and Deep Attention Networks
Probabilistic load forecasting is crucial for modern energy systems, providing point forecasts and uncertainty quantification to enhance decision-making and grid reliability. Achieving accurate forecasts often requires d…
Decision MakingDeep AttentionLoad ForecastingUncertainty QuantificationZero-shot Load Forecasting for Integrated Energy Systems: A Large Language Model-based Framework with Multi-task Learning
The growing penetration of renewable energy sources in power systems has increased the complexity and uncertainty of load forecasting, especially for integrated energy systems with multiple energy carriers. Traditional f…
Language ModelingLanguage ModellingLarge Language ModelLoad Forecasting+1Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning
Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) record household energy data. Traditional m…
Federated LearningLoad ForecastingMeta-LearningPersonalized Federated LearningEnhanced Load Forecasting with GAT-LSTM: Leveraging Grid and Temporal Features
Accurate power load forecasting is essential for the efficient operation and planning of electrical grids, particularly given the increased variability and complexity introduced by renewable energy sources. This paper in…
Graph AttentionLoad ForecastingMulti-task Online Learning for Probabilistic Load Forecasting
Load forecasting is essential for the efficient, reliable, and cost-effective management of power systems. Load forecasting performance can be improved by learning the similarities among multiple entities (e.g., regions,…
Load ForecastingManagementMulti-Task LearningHourly Short Term Load Forecasting for Residential Buildings and Energy Communities
Electricity load consumption may be extremely complex in terms of profile patterns, as it depends on a wide range of human factors, and it is often correlated with several exogenous factors, such as the availability of r…
energy managementLoad ForecastingHOPS: High-order Polynomials with Self-supervised Dimension Reduction for Load Forecasting
Load forecasting is a fundamental task in smart grid. Many techniques have been applied to developing load forecasting models. Due to the challenges such as the Curse of Dimensionality, overfitting, and limited computing…
Computational EfficiencyDimensionality ReductionLoad ForecastingKolmogorov-Arnold Recurrent Network for Short Term Load Forecasting Across Diverse Consumers
Load forecasting plays a crucial role in energy management, directly impacting grid stability, operational efficiency, cost reduction, and environmental sustainability. Traditional Vanilla Recurrent Neural Networks (RNNs…
energy managementKolmogorov-Arnold NetworksLoad ForecastingManagement