Load Forecasting
36 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Load Forecasting
ASHRAE energy prediction III
PJM(AEP)
Latest papers
Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting
asifiqbal8739/splitloadforecasting
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Under this framework, each GS is responsible for training a personalized model split for their respective neighbourhoods, whereas the SP can train a single global or personalized model for each GS.
DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load
Accordingly, we devise a novel Diffusion model termed DiffPLF for Probabilistic Load Forecasting of EV charging, which can explicitly approximate the predictive load distribution conditioned on historical data and related covariates.
Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting
In the realm of power systems, the increasing involvement of residential users in load forecasting applications has heightened concerns about data privacy.
E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning
xuwkk/e2e-at
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Successful machine learning involves a complete pipeline of data, model, and downstream applications.
Utilizing Language Models for Energy Load Forecasting
Energy load forecasting plays a crucial role in optimizing resource allocation and managing energy consumption in buildings and cities.
Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
In traditional deep learning algorithms, one of the key assumptions is that the data distribution remains constant during both training and deployment.
Task-Aware Machine Unlearning and Its Application in Load Forecasting
xuwkk/task_aware_machine_unlearning
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To balance between unlearning completeness and model performance, a performance-aware algorithm is proposed by evaluating the sensitivity of local model parameter change using influence function and sample re-weighting.
Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features.
DeepTSF: Codeless machine learning operations for time series forecasting
DeepTSF automates key aspects of the ML lifecycle, making it an ideal tool for data scientists and MLops engineers engaged in machine learning (ML) and deep learning (DL)-based forecasting.
Differential Evolution Algorithm based Hyper-Parameters Selection of Transformer Neural Network Model for Load Forecasting
We apply several metaheuristics namely Differential Evolution to find the optimal hyperparameters of the Transformer-based Neural Network to produce accurate forecasts.
