Spatial-temporal residential short-term load forecasting via graph neural networks
Electric load forecasting, especially short-term load forecasting, is of significant importance
for the safe and efficient operation of power grids. With the wide adoption of advanced smart …
for the safe and efficient operation of power grids. With the wide adoption of advanced smart …
Efficient residential electric load forecasting via transfer learning and graph neural networks
D Wu, W Lin - IEEE Transactions on Smart Grid, 2022 - ieeexplore.ieee.org
The accurate short-term electric load forecasting (STLF) is critical for the safety and
economical operation of modern electric power systems. Recently, the graph neural network …
economical operation of modern electric power systems. Recently, the graph neural network …
Short-term residential load forecasting based on LSTM recurrent neural network
As the power system is facing a transition toward a more intelligent, flexible, and interactive
system with higher penetration of renewable energy generation, load forecasting, especially …
system with higher penetration of renewable energy generation, load forecasting, especially …
A deep learning method for short-term residential load forecasting in smart grid
Residential demand response is vital for the efficiency of power system. It has attracted
much attention from both academic and industry in recent years. Accurate short-term load …
much attention from both academic and industry in recent years. Accurate short-term load …
Short-term residential load forecasting based on resident behaviour learning
Residential load forecasting has been playing an increasingly important role in modern
smart grids. Due to the variability of residents' activities, individual residential loads are …
smart grids. Due to the variability of residents' activities, individual residential loads are …
Short-term residential load forecasting using graph convolutional recurrent neural networks
S Arastehfar, M Matinkia, MR Jabbarpour - Engineering Applications of …, 2022 - Elsevier
The abundance of energy consumption data collected by smart meters has inspired
researchers to employ deep neural networks to solve the existing problems in the power …
researchers to employ deep neural networks to solve the existing problems in the power …
Review of family-level short-term load forecasting and its application in household energy management system
P Ma, S Cui, M Chen, S Zhou, K Wang - Energies, 2023 - mdpi.com
With the rapid development of smart grids and distributed energy sources, the home energy
management system (HEMS) is becoming a hot topic of research as a hub for connecting …
management system (HEMS) is becoming a hot topic of research as a hub for connecting …
A CNN-Sequence-to-Sequence network with attention for residential short-term load forecasting
Residential short-term load forecasting has become an essential process to develop
successful demand response strategies, and help utilities and customers optimize energy …
successful demand response strategies, and help utilities and customers optimize energy …
Short‐term building load forecast based on a data‐mining feature selection and LSTM‐RNN method
G Sun, C Jiang, X Wang, X Yang - IEEJ Transactions on …, 2020 - Wiley Online Library
Short‐term load forecast for individual electric customers is becoming increasingly important
in the grid operation, since the power system is becoming a more interactive and intelligent …
in the grid operation, since the power system is becoming a more interactive and intelligent …
A short-term residential load forecasting model based on LSTM recurrent neural network considering weather features
Y Wang, N Zhang, X Chen - Energies, 2021 - mdpi.com
With economic growth, the demand for power systems is increasingly large. Short-term load
forecasting (STLF) becomes an indispensable factor to enhance the application of a smart …
forecasting (STLF) becomes an indispensable factor to enhance the application of a smart …