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Load Forecasting

Papers

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Showing 101–125 of 235 papers

TitleStatusHype
Short-term power load forecasting method based on CNN-SAEDN-Res—0
Probabilistic load forecasting with Reservoir Computing—0
Differential Evolution Algorithm based Hyper-Parameters Selection of Transformer Neural Network Model for Load ForecastingCode0
Residential Load Forecasting: An Online-Offline Deep Kernel Learning MethodCode0
DeepTSF: Codeless machine learning operations for time series forecastingCode0
An Error Correction Mid-term Electricity Load Forecasting Model Based on Seasonal Decomposition—0
Transformer Training Strategies for Forecasting Multiple Load Time SeriesCode0
SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events—0
Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging—0
Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting—0
Leveraging Predictions in Power System Frequency Control: an Adaptive Approach—0
Short-Term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources—0
DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks—0
A Unifying Framework of Attention-based Neural Load ForecastingCode0
LSTM-based Load Forecasting Robustness Against Noise Injection Attack in Microgrid—0
A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas—0
Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels—0
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives—0
An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman PseudospectraCode0
Spintronic Physical Reservoir for Autonomous Prediction and Long-Term Household Energy Load Forecasting—0
Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids—0
In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance—0
A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers—0
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models—0
Information Theoretical Importance Sampling Clustering—0
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