Machine learning autoencoder‐based parameters prediction for solar power generation systems in smart grid
During the fourth energy revolution, artificial intelligence implementation is necessary in all
fields of technology to meet the increasing energy demands and address the diminishing …
fields of technology to meet the increasing energy demands and address the diminishing …
Routing attacks detection in MANET using trust management enabled hybrid machine learning
G Arulselvan, A Rajaram - Wireless Networks, 2024 - Springer
The ever-changing topology in mobile ad hoc networks (MANETs) makes routing a
formidable obstacle. The infrastructure-independent capabilities of MANET ensure the …
formidable obstacle. The infrastructure-independent capabilities of MANET ensure the …
Uniform Physics Informed Neural Network Framework for Microgrid and its application in voltage stability analysis
This paper focus on the application of Physics Informed Neural Network (PINN) for extracting
parameters of photovoltaic (PV), wind, and energy storage equipment models. Accurately …
parameters of photovoltaic (PV), wind, and energy storage equipment models. Accurately …
Optimizing solar power generation forecasting in smart grids: A hybrid convolutional neural network-autoencoder long short-term memory approach
Incorporating zero-carbon emission sources of energy into the electric grid is essential to
meet the growing energy needs in public and industrial sectors. Smart grids, with their …
meet the growing energy needs in public and industrial sectors. Smart grids, with their …
基于强化学习的边缘计算智能电网资源调度算法
余竞航， 赵一辰， 宋浒 - 电信科学, 2024 - infocomm-journal.com
智能电网是一种能够进行智能管理和优化的电力网络. 网络虚拟化技术可以有效提高智能电网的
资源利用率和可靠性, 从而满足不同用户的差异化需求. 在资源有限的情况下 …
资源利用率和可靠性, 从而满足不同用户的差异化需求. 在资源有限的情况下 …
Improved solar power prediction using cnn-lstm models for optimized smart grid performance
During the fourth energy revolution, the integration of Artificial Intelligence (AI) across
various technological fields is critical to meet rising energy demands and address the …
various technological fields is critical to meet rising energy demands and address the …
Forecasting Solar Power Generation Using Extreme Gradient Boosting: A Machine Learning Approach
The swift expansion of solar photovoltaic (PV) technology has introduced significant
challenges for those overseeing electricity distributions due to its reliance on weather …
challenges for those overseeing electricity distributions due to its reliance on weather …
Review and Comparative Analysis of Deep Learning Techniques for Smart Grid Load Forecasting
H Shahinzadeh, H Sadrarhami… - 2024 20th CSI …, 2024 - ieeexplore.ieee.org
In the last decade, the water and electricity industry has experienced significant investments
in smart grid technologies. Within a smart grid framework, information and energy engage in …
in smart grid technologies. Within a smart grid framework, information and energy engage in …
Integrating Temporal and Feedforward Models for Solar Energy Prediction: LSTM and ANN Hybrid Approaches
The increasing reliance on renewable energy, particularly solar power, necessitates
accurate models for predicting energy output to optimize storage and distribution systems …
accurate models for predicting energy output to optimize storage and distribution systems …
A Deep Learning-Based Multi-Layer Dynamic Multivariate Prediction Model for Power System Time Series Data Prediction Model
H Du, H Dong, D Ma, X Hu - 2024 6th International Conference …, 2024 - ieeexplore.ieee.org
Multivariate time series forecasting is an important but challenging problem in future power
system. Existing fore-casting models struggle to handle the increasing amount of time series …
system. Existing fore-casting models struggle to handle the increasing amount of time series …