Residential load forecasting based on LSTM fusing self-attention mechanism with pooling
Day-ahead residential load forecasting is crucial for electricity dispatch and demand
response in power systems. Electrical loads are characterized by volatility and uncertainty …
response in power systems. Electrical loads are characterized by volatility and uncertainty …
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 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 …
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 …
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 load forecasting for a single household based on convolution neural networks using data augmentation
SK Acharya, YM Wi, J Lee - Energies, 2019 - mdpi.com
Advanced metering infrastructure (AMI) is spreading to households in some countries, and
could be a source for forecasting the residential electric demand. However, load forecasting …
could be a source for forecasting the residential electric demand. However, load forecasting …
Probabilistic residential load forecasting based on micrometeorological data and customer consumption pattern
A prior knowledge of residential load demand is critical for power system operations at the
distribution level, such as economic dispatch, demand response and energy storage …
distribution level, such as economic dispatch, demand response and energy storage …
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 short-term load forecasting model based on mixup and transfer learning
Y Lu, G Wang, S Huang - Electric Power Systems Research, 2022 - Elsevier
When the amount of historical load data is insufficient, the use of deep learning for load
forecasting is prone to overfitting. This paper proposes a short-term electric load forecasting …
forecasting is prone to overfitting. This paper proposes a short-term electric load forecasting …