Energy Load Forecasting

Energy load forecasting aims to accurately predict future energy consumption, crucial for optimizing resource allocation and improving energy efficiency in buildings and cities. Current research emphasizes the use of advanced machine learning techniques, including deep learning models like LSTMs and hybrid/ensemble methods, often incorporating external data sources such as mobility patterns to enhance prediction accuracy, particularly during periods of unusual consumption. This field is significant because improved forecasting enables better grid management, reduces energy waste, and facilitates the transition to more sustainable energy systems. Furthermore, research is actively addressing data privacy concerns through federated learning approaches, allowing for collaborative model training without compromising sensitive consumer information.

Papers

October 26, 2023

Utilizing Language Models for Energy Load Forecasting
Hao Xue, Flora D. Salim
Language Model Energy Consumption Electricity Demand Autoregressive Generation Energy Load Forecasting

September 8, 2023

Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning
Arian Prabowo, Kaixuan Chen, Hao Xue, Subbu Sethuvenkatraman, Flora D. Salim
Continual LEArning Catastrophic Forgetting Covid 19 Continual Learning Pandemic Lockdown Energy Load Forecasting

March 1, 2022

FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers
Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh, Shama Naz Islam, Abdun Naser Mahmood, Robin Doss
Privacy Preserving Federated Learning Energy Load Forecasting

February 23, 2022

Systematic review of deep learning and machine learning for building energy
Ardabili Sina, Leila Abdolalizadeh, Csaba Mako, Bernat Torok, Mosavi Amir
Deep Learning New Machine Systematic Review Energy Policy Research Energy Management Consumption Forecasting Accurate Energy Energy Modeling Energy Load Forecasting

Energy Load Forecasting

Papers

Utilizing Language Models for Energy Load Forecasting

Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learning

FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers

Systematic review of deep learning and machine learning for building energy