Machine learning and deep learning in energy systems: A review
With population increases and a vital need for energy, energy systems play an important
and decisive role in all of the sectors of society. To accelerate the process and improve the …
and decisive role in all of the sectors of society. To accelerate the process and improve the …
[HTML][HTML] Next-generation energy systems for sustainable smart cities: Roles of transfer learning
Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while
improving grid stability and meeting service demand. This is possible by adopting next …
improving grid stability and meeting service demand. This is possible by adopting next …
Smart grid cyber security enhancement: Challenges and solutions—A review
T Alsuwian, A Shahid Butt, AA Amin - Sustainability, 2022 - mdpi.com
The incorporation of communication technology with Smart Grid (SG) is proposed as an
optimal solution to fulfill the requirements of the modern power system. A smart grid …
optimal solution to fulfill the requirements of the modern power system. A smart grid …
Autoencoder-driven fault detection and diagnosis in building automation systems: Residual-based and latent space-based approaches
Recently, data-driven fault detection and diagnosis (FDD) technologies have been studied
extensively to detect the fault status early and maintain the health of building automation …
extensively to detect the fault status early and maintain the health of building automation …
A comprehensive review of high-frequency AC microgrids for distribution systems
Continuous advancements in distributed generation, energy storage, electric vehicles, and
industrial modernization have forced the electrical distribution system to operate efficiently …
industrial modernization have forced the electrical distribution system to operate efficiently …
Characterizing residential load patterns on multi-time scales utilizing LSTM autoencoder and electricity consumption data
Load patterns represent a clear picture of electricity usage, reflecting the consumer's habits.
Previous works mainly focused on load patterns discovery on a fixed scale, but limited to …
Previous works mainly focused on load patterns discovery on a fixed scale, but limited to …
Smart meter data classification using optimized random forest algorithm
A Zakariazadeh - ISA transactions, 2022 - Elsevier
Implementing a proper clustering algorithm and a high accuracy classifier for applying on
electricity smart meter data is the first stage in analyzing and managing electricity …
electricity smart meter data is the first stage in analyzing and managing electricity …
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 …
An adaptive data compression technique based on optimal thresholding using multi-objective PSO algorithm for power system data
S Karthika, P Rathika - Applied Soft Computing, 2024 - Elsevier
The widespread development of monitoring devices in power system has indeed led to the
generation of large amounts of power consumption data. Storing and transmitting this …
generation of large amounts of power consumption data. Storing and transmitting this …
[PDF][PDF] 基于改进生成式对抗网络的电气数据升频重建方法
李富盛， 林丹， 余涛， 王克英， 吴毓峰… - 电力系统自动化, 2022 - epjournal.csee.org.cn
高频电气数据是提高电网态势感知准确度, 监测水平和辅助服务质量等的数据基础之一, 但是,
传统重建算法难以实现高精度的数据重建. 因此, 文中利用改进生成式对抗网络将低频电气数据 …
传统重建算法难以实现高精度的数据重建. 因此, 文中利用改进生成式对抗网络将低频电气数据 …