Load Forecasting

Load forecasting aims to accurately predict future electricity demand, crucial for efficient grid management and renewable energy integration. Current research emphasizes improving forecasting accuracy through advanced machine learning models, including deep learning architectures like Transformers, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs, such as LSTMs and GRUs), and hybrid approaches combining these techniques. These models often leverage diverse data sources, such as historical load data, weather information, and calendar effects, and are being enhanced with techniques like transfer learning, federated learning, and attention mechanisms to address data scarcity, privacy concerns, and model interpretability. Accurate load forecasting is vital for ensuring grid stability, optimizing energy resource allocation, and facilitating the transition to cleaner energy systems.

Papers

October 13, 2024

Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid
Vineet Jagadeesan Nair, Lucas Pereira
Federated Learning Early Stage Convergence Power Grid Load Forecasting Faster Convergence Energy Storage Solar Generation

October 4, 2024

Research on short-term load forecasting model based on VMD and IPSO-ELM
Qiang Xie
DH Research Load Forecasting Extreme Learning Machine Variational Mode Decomposition

September 29, 2024

Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators
Manuel Treutlein, Marc Schmidt, Roman Hahn, Matthias Hertel, Benedikt Heidrich, Ralf Mikut, Veit Hagenmeyer
Power System Load Forecasting Metadata Information Distribution System Load Prediction Compressed Measurement Distribution Grid

September 26, 2024

A multi-source data power load forecasting method using attention mechanism-based parallel cnn-gru
Chao Min, Yijia Wang, Bo Zhang, Xin Ma, Junyi Cui
Human Attention Load Forecasting Parallel Hybrid Network

September 23, 2024

Automated Spatio-Temporal Weather Modeling for Load Forecasting
Julie Keisler (CRIStAL, EDF R\&D OSIRIS, EDF R\&D), Margaux Bregere (EDF R\&D, EDF R\&D OSIRIS, LPSM (UMR\_8001))
Load Forecasting Spatiotemporal Forecasting Renewable Generation Weather Prediction Model

September 18, 2024

Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network
Mohammad Wazed Ali (Intelligent Embedded Systems (IES), University of Kassel, Kassel, Germany), Asif bin Mustafa (School of CIT, Technical University of Munich, Munich, Germany), Md. Aukerul Moin Shuvo (Dept. of Computer Science and Engineering, Rajshahi University of Engg. & Technology, Rajshahi, Bangladesh), Bernhard Sick (Intelligent Embedded Systems (IES), University of Kassel, Kassel, Germany)
Load Forecasting Location Information Deep Transfer Load Prediction Electric Vehicle Charging Station Probabilistic Load Mid Term Electricity

July 16, 2024

Learning Global and Local Features of Power Load Series Through Transformer and 2D-CNN: An Image-based Multi-step Forecasting Approach Incorporating Phase Space Reconstruction
Zihan Tang, Tianyao Ji, Wenhu Tang
Load Forecasting Local Feature Multi Step Phase Space

July 11, 2024

Improve Load Forecasting in Energy Communities through Transfer Learning using Open-Access Synthetic Profiles
Lukas Moosbrugger, Valentin Seiler, Gerhard Huber, Peter Kepplinger
Transfer Learning Load Forecasting Load Prediction Energy Flexibility Energy Community

June 10, 2024

Short-Term Electricity Demand Forecasting of Dhaka City Using CNN with Stacked BiLSTM
Kazi Fuad Bin Akhter, Sadia Mobasshira, Saief Nowaz Haque, Mahjub Alam Khan Hesham, Tanvir Ahmed
Convolutional Neural Network Long Short Term Memory LSTM Network Load Forecasting Forecasting Performance Mid Term Electricity

June 9, 2024

Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach
Georgios Tsoumplekas, Christos L. Athanasiadis, Dimitrios I. Doukas, Antonios Chrysopoulos, Pericles A. Mitkas
Meta Learning Load Forecasting Smart Grid Data Scarcity Agnostic Meta learnIng Short Term Load Forecasting Historical Load

June 2, 2024

Multi-variable Adversarial Time-Series Forecast Model
Xiaoqiao Chen
Time Series Load Forecasting Power System Short Term Load Forecasting

May 29, 2024

Multiscale Spatio-Temporal Enhanced Short-term Load Forecasting of Electric Vehicle Charging Stations
Zongbao Zhang, Jiao Hao, Wenmeng Zhao, Yan Liu, Yaohui Huang, Xinhang Luo
Spatio Temporal Load Forecasting Short Term Load Forecasting Electric Vehicle Charging Station

May 14, 2024

Automated Deep Learning for Load Forecasting
Julie Keisler, Sandra Claudel, Gilles Cabriel, Margaux Brégère
Deep Neural Network Load Forecasting Six DRAGON Fly Automated Deep Learning

April 7, 2024

TimeGPT in Load Forecasting: A Large Time Series Model Perspective
Wenlong Liao, Fernando Porte-Agel, Jiannong Fang, Christian Rehtanz, Shouxiang Wang, Dechang Yang, Zhe Yang
Load Forecasting Large Time Series Model Historical Load

April 4, 2024

Exploring Lightweight Federated Learning for Distributed Load Forecasting
Abhishek Duttagupta, Jin Zhao, Shanker Shreejith
Load Forecasting Resource Constrained Smart Meter

April 1, 2024

Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers
Shourya Bose, Yu Zhang, Kibaek Kim
Energy Consumption Load Forecasting Smart Meter Short Term Load Forecasting Heterogeneity Challenge Personalization Layer

March 3, 2024

Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting
Asif Iqbal, Prosanta Gope, Biplab Sikdar
Split Learning Load Forecasting Smart Meter

February 22, 2024

Adaptive time series forecasting with markovian variance switching
Baptiste Abélès, Joseph de Vilmarest, Olivier Wintemberger
State Space Model Load Forecasting Variational Bayes Gaussian State Space Model Adaptive Forecasting

February 21, 2024

Load Forecasting

Papers

Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid

Research on short-term load forecasting model based on VMD and IPSO-ELM

Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators

A multi-source data power load forecasting method using attention mechanism-based parallel cnn-gru

Automated Spatio-Temporal Weather Modeling for Load Forecasting

Location based Probabilistic Load Forecasting of EV Charging Sites: Deep Transfer Learning with Multi-Quantile Temporal Convolutional Network

Learning Global and Local Features of Power Load Series Through Transformer and 2D-CNN: An Image-based Multi-step Forecasting Approach Incorporating Phase Space Reconstruction

Improve Load Forecasting in Energy Communities through Transfer Learning using Open-Access Synthetic Profiles

Short-Term Electricity Demand Forecasting of Dhaka City Using CNN with Stacked BiLSTM

Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach

Multi-variable Adversarial Time-Series Forecast Model

Multiscale Spatio-Temporal Enhanced Short-term Load Forecasting of Electric Vehicle Charging Stations

Automated Deep Learning for Load Forecasting

TimeGPT in Load Forecasting: A Large Time Series Model Perspective

Exploring Lightweight Federated Learning for Distributed Load Forecasting

Addressing Heterogeneity in Federated Load Forecasting with Personalization Layers

Privacy-Preserving Collaborative Split Learning Framework for Smart Grid Load Forecasting

Adaptive time series forecasting with markovian variance switching

AI-Powered Predictions for Electricity Load in Prosumer Communities

DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load