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 with no code
Load Data Valuation in Multi-Energy Systems: An End-to-End Approach
Accurate load forecasting serves as the foundation for the flexible operation of multi-energy systems (MES).
Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting
Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution.
Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System
Specifically, we train an advanced forecasting model (a temporal fusion transformer) using data from multiple different households, and then finetune this global model on a new household with limited data (i. e. only a few days).
Secure short-term load forecasting for smart grids with transformer-based federated learning
Electricity load forecasting is an essential task within smart grids to assist demand and supply balance.
Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting
Electric load forecasting is an indispensable component of electric power system planning and management.
Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series
Short-term load forecasting (STLF) is crucial for the daily operation of power grids.
Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting
Overall, case studies demonstrated the effectiveness of ML models trained with different weather and time input features for ERCOT load forecasting.
Probabilistic Load Forecasting of Distribution Power Systems based on Empirical Copulas
Accurate and reliable electricity load forecasts are becoming increasingly important as the share of intermittent resources in the system increases.
Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clients
The advent of smart meters has enabled pervasive collection of energy consumption data for training short-term load forecasting (STLF) models.
Short-Term Load Forecasting Using A Particle-Swarm Optimized Multi-Head Attention-Augmented CNN-LSTM Network
Short-term load forecasting is of paramount importance in the efficient operation and planning of power systems, given its inherent non-linear and dynamic nature.
