Artificial intelligence techniques in smart grid: A survey

OA Omitaomu, H Niu - Smart Cities, 2021 - mdpi.com
The smart grid is enabling the collection of massive amounts of high-dimensional and multi-
type data about the electric power grid operations, by integrating advanced metering …

Integrating artificial intelligence Internet of Things and 5G for next-generation smartgrid: A survey of trends challenges and prospect

E Esenogho, K Djouani, AM Kurien - Ieee Access, 2022 - ieeexplore.ieee.org
Smartgrid is a paradigm that was introduced into the conventional electricity network to
enhance the way generation, transmission, and distribution networks interrelate. It involves …

Convolutional autoencoder based feature extraction and clustering for customer load analysis

S Ryu, H Choi, H Lee, H Kim - IEEE Transactions on Power …, 2019 - ieeexplore.ieee.org
As the number of smart meters increases, compression of metering data becomes essential
for data transmission, storing and processing perspectives. Specifically, feature extraction …

Denoising autoencoder-based missing value imputation for smart meters

S Ryu, M Kim, H Kim - IEEE Access, 2020 - ieeexplore.ieee.org
Electric load data are essential for data-driven approaches (including deep learning) in
smart grid, and advanced smart meter technologies provide fine-grained data with reliable …

A systematic review of machine learning-based missing value imputation techniques

T Thomas, E Rajabi - Data Technologies and Applications, 2021 - emerald.com
Purpose The primary aim of this study is to review the studies from different dimensions
including type of methods, experimentation setup and evaluation metrics used in the novel …

Smart meter data analytics applications for secure, reliable and robust grid system: Survey and future directions

S Mitra, B Chakraborty, P Mitra - Energy, 2024 - Elsevier
The new power sector scenario has focused on integrating renewable energy sources into
the grid and achieving trustworthy consumer-utility-stakeholder relationships using smart …

Developing reliable hourly electricity demand data through screening and imputation

TH Ruggles, DJ Farnham, D Tong, K Caldeira - Scientific data, 2020 - nature.com
Electricity usage (demand) data are used by utilities, governments, and academics to model
electric grids for a variety of planning (eg, capacity expansion and system operation) …

Influence of tensile properties on hole expansion ratio investigated using a generative adversarial imputation network with explainable artificial intelligence

JA Lee, J Park, YT Choi, RE Kim, J Jung, S Lee… - Journal of Materials …, 2023 - Springer
Hole expansion ratio is widely used to estimate the stretch flangeability of sheet metals
which is a critical property of formability and to evaluate the efficiency of a forming process …

Towards missing electric power data imputation for energy management systems

MC Wang, CF Tsai, WC Lin - Expert Systems with Applications, 2021 - Elsevier
Demand for electricity is gradually increasing in many countries. Efforts in related studies
have been made for the application of data mining techniques over related electric power …

Bagging ensemble of multilayer perceptrons for missing electricity consumption data imputation

S Jung, J Moon, S Park, S Rho, SW Baik, E Hwang - Sensors, 2020 - mdpi.com
For efficient and effective energy management, accurate energy consumption forecasting is
required in energy management systems (EMSs). Recently, several artificial intelligence …