Probabilistic Load

Probabilistic load forecasting aims to predict future electricity demand not just as a single point estimate, but as a probability distribution, accounting for inherent uncertainties. Current research heavily emphasizes deep learning models, including convolutional neural networks, recurrent neural networks (like LSTMs), diffusion models, and quantile regression techniques, often combined with transfer learning or meta-learning approaches to improve accuracy and adaptability across diverse locations and datasets. This focus on probabilistic forecasting is crucial for enhancing the reliability and resilience of power grids, particularly with the increasing integration of renewable energy sources and electric vehicles, enabling more informed decision-making in energy management and resource allocation. Improved forecasting accuracy and uncertainty quantification are key goals driving ongoing research.

Papers

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 Probabilistic Load Electric Vehicle Charging Station Mid Term Electricity

August 8, 2024

Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces
Luca Pernigo, Rohan Sen, Davide Baroli
Power Grid Kernel Hilbert Space Energy Prediction Accurate Energy Probabilistic Load

July 31, 2024

Who should I trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models
Kaustav Bhattacharjee, Soumya Kundu, Indrasis Chakraborty, Aritra Dasgupta
Forecasting Model Visual Analytics Probabilistic Load Net Load Forecasting

July 19, 2024

On the Impact of PRB Load Uncertainty Forecasting for Sustainable Open RAN
Vaishnavi Kasuluru, Luis Blanco, Cristian J. Vaca-Rubio, Engin Zeydan
Global Impact Power Consumption Radio Access Network Load Prediction RAN Architecture Probabilistic Load

June 15, 2024

Stacking for Probabilistic Short-term Load Forecasting
Grzegorz Dudek
Nondeterministic Stack Probabilistic Load Regression Forest Quantile Forecast

May 3, 2024

A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction
Weijie Xia, Chenguang Wang, Peter Palensky, Pedro P. Vergara
Human Prediction Energy Consumption Conditional Reasoning Flow Based Generative Probabilistic Load Electricity Consumption

February 21, 2024

DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load
Siyang Li, Hui Xiong, Yize Chen
Conditional Diffusion Model Probabilistic Forecast Load Forecasting Probabilistic Time Series Forecasting Electric Vehicle Charging Load Prediction Probabilistic Load

August 24, 2023

Probabilistic load forecasting with Reservoir Computing
Michele Guerra, Simone Scardapane, Filippo Maria Bianchi
Uncertainty Quantification Reservoir Computing Probabilistic Load Point Forecasting

December 6, 2022

Denoising diffusion probabilistic models for probabilistic energy forecasting
Esteban Hernandez Capel, Jonathan Dumas
Generative Model Generative Adversarial Network Probabilistic Model Denoising Diffusion Probabilistic Forecast Consumption Forecasting Forecasting Competition Probabilistic Load

October 17, 2022

Probabilistic Forecasting Methods for System-Level Electricity Load Forecasting
Philipp Giese
Long Term Key Factor Load Forecasting Energy Policy Research Probabilistic Load Probabilistic Forecasting

July 20, 2022

Operating Envelopes under Probabilistic Electricity Demand and Solar Generation Forecasts
Yu Yi, Gregor Verbic
Conditional Generative Adversarial Network Optimal Power Flow Smart Meter Solar Generation Speech Envelope Probabilistic Load Active Distribution

April 29, 2022

Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows
Marcel Arpogaus, Marcus Voss, Beate Sick, Mark Nigge-Uricher, Oliver Dürr
Normalizing Flow Load Forecasting Conditional Density Probabilistic Load Low Voltage

Probabilistic Load

Papers

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

Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces

Who should I trust? A Visual Analytics Approach for Comparing Net Load Forecasting Models

On the Impact of PRB Load Uncertainty Forecasting for Sustainable Open RAN

Stacking for Probabilistic Short-term Load Forecasting

A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction

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

Probabilistic load forecasting with Reservoir Computing

Denoising diffusion probabilistic models for probabilistic energy forecasting

Probabilistic Forecasting Methods for System-Level Electricity Load Forecasting

Operating Envelopes under Probabilistic Electricity Demand and Solar Generation Forecasts

Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows