Load Forecasting
36 papers with code • 0 benchmarks • 2 datasets
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Latest papers with no code
Electrical Load Forecasting Model Using Hybrid LSTM Neural Networks with Online Correction
Accurate electrical load forecasting is of great importance for the efficient operation and control of modern power systems.
Adaptive time series forecasting with markovian variance switching
In this paper, we propose a new way of estimating variances based on online learning theory; we adapt expert aggregation methods to learn the variances over time.
AI-Powered Predictions for Electricity Load in Prosumer Communities
The flexibility in electricity consumption and production in communities of residential buildings, including those with renewable energy sources and energy storage (a. k. a., prosumers), can effectively be utilized through the advancement of short-term demand response mechanisms.
Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition
Though the strong capabilities of learning the non-linearity of the load patterns and the high prediction accuracy have been achieved, the interpretability of typical deep learning models for electricity load forecasting is less studied.
Deep Learning-Based Cyber-Attack Detection Model for Smart Grids
In this paper, a novel artificial intelligence-based cyber-attack detection model for smart grids is developed to stop data integrity cyber-attacks (DIAs) on the received load data by supervisory control and data acquisition (SCADA).
Privacy-Preserving Load Forecasting via Personalized Model Obfuscation
The widespread adoption of smart meters provides access to detailed and localized load consumption data, suitable for training building-level load forecasting models.
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.