Smart grid big data analytics: Survey of technologies, techniques, and applications
Smart grids have been gradually replacing the traditional power grids since the last decade.
Such transformation is linked to adding a large number of smart meters and other sources of …
Such transformation is linked to adding a large number of smart meters and other sources of …
[HTML][HTML] Random forest regressor-based approach for detecting fault location and duration in power systems
Power system failures or outages due to short-circuits or “faults” can result in long service
interruptions leading to significant socio-economic consequences. It is critical for electrical …
interruptions leading to significant socio-economic consequences. It is critical for electrical …
A multiprocessing-based sensitivity analysis of machine learning algorithms for load forecasting of electric power distribution system
For the utility to plan the resources accurately and balance the electricity supply and
demand, accurate and timely forecasting is required. The proliferation of smart meters in the …
demand, accurate and timely forecasting is required. The proliferation of smart meters in the …
[HTML][HTML] A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids
The electric grid is a key enabling infrastructure for the ambitious transition towards carbon
neutrality as we grapple with climate change. With deepening penetration of renewable …
neutrality as we grapple with climate change. With deepening penetration of renewable …
[HTML][HTML] Privacy preservation of data-driven models in smart grids using homomorphic encryption
Deep learning models have been applied for varied electrical applications in smart grids
with a high degree of reliability and accuracy. The development of deep learning models …
with a high degree of reliability and accuracy. The development of deep learning models …
[PDF][PDF] PSML: a multi-scale time-series dataset for machine learning in decarbonized energy grids
The electric grid is a key enabling infrastructure for the ambitious transition towards carbon
neutrality as we grapple with climate change. With deepening penetration of renewable …
neutrality as we grapple with climate change. With deepening penetration of renewable …
Intelligent automation of crime prediction using data mining
AH Al-Ghushami, D Syed, J Sessa… - 2022 IEEE 31st …, 2022 - ieeexplore.ieee.org
Crime Pattern Theory is a way of elucidating the reasons why specific types of crime happen
at certain areas only. According to the theory, the offenders, rather than venturing into …
at certain areas only. According to the theory, the offenders, rather than venturing into …
Performance evaluation of tree-based models for big data load forecasting using randomized hyperparameter tuning
In this paper machine learning (ML) models have been developed for the application of big
data load forecasting using parallel computation. The load forecasting models' performance …
data load forecasting using parallel computation. The load forecasting models' performance …
Distribution Grid Fault Classification and Localization using Convolutional Neural Networks
This manuscript addresses the critical challenge of fault classification and localization within
smart distribution networks, exacerbated by the complex integration of distributed energy …
smart distribution networks, exacerbated by the complex integration of distributed energy …
Detection and Classification of Physical and Electrical Fault in PV Array System by Random Forest-Based Approach
SS SYED, B Li, A Zheng - International Journal of …, 2024 - ijeepse.ejournal.unri.ac.id
The importance of solar photovoltaic (PV) systems has increased over the past ten years
due to the solar PV industry's explosive growth. To ensure the reliable, safe, and efficient …
due to the solar PV industry's explosive growth. To ensure the reliable, safe, and efficient …