Hybrid CNN-LSTM model for short-term individual household load forecasting
M Alhussein, K Aurangzeb, SI Haider - Ieee Access, 2020 - ieeexplore.ieee.org
Power grids are transforming into flexible, smart, and cooperative systems with greater
dissemination of distributed energy resources, advanced metering infrastructure, and …
dissemination of distributed energy resources, advanced metering infrastructure, and …
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 combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior
W Yang, J Shi, S Li, Z Song, Z Zhang, Z Chen - Applied Energy, 2022 - Elsevier
With the growth of residential load and the popularity of intelligent devices, resident users
have become important target customers for demand response (DR). However, due to the …
have become important target customers for demand response (DR). However, due to the …
Hybrid multitask multi-information fusion deep learning for household short-term load forecasting
L Jiang, X Wang, W Li, L Wang, X Yin… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
With the detailed load data provided by smart meter, the learning of electricity usage
behavior for individual household short-term load forecasting has become a hot research …
behavior for individual household short-term load forecasting has become a hot research …
Improving the efficiency of multistep short-term electricity load forecasting via R-CNN with ML-LSTM
Multistep power consumption forecasting is smart grid electricity management's most
decisive problem. Moreover, it is vital to develop operational strategies for electricity …
decisive problem. Moreover, it is vital to develop operational strategies for electricity …
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 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 …
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 …
A pyramid-CNN based deep learning model for power load forecasting of similar-profile energy customers based on clustering
K Aurangzeb, M Alhussein, K Javaid, SI Haider - IEEE Access, 2021 - ieeexplore.ieee.org
With rapid advancements in renewable energy sources, billing mechanism (AMI), and latest
communication technologies, the traditional control networks are evolving towards wise …
communication technologies, the traditional control networks are evolving towards wise …
Residential load forecasting using deep neural networks (DNN)
Forecasting of consumer electricity usages plays an important role to make total smart grid
system more reliable. As the activities of individual residential consumers has many …
system more reliable. As the activities of individual residential consumers has many …