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 …

A pyramid-CNN based deep learning model for power load forecasting of similar-profile energy customers based on clustering

K Aurangzeb, M Alhussein, K Javaid, SI Haider - IEEE Access, 2021 - ieeexplore.ieee.org
With rapid advancements in renewable energy sources, billing mechanism (AMI), and latest
communication technologies, the traditional control networks are evolving towards wise …

[HTML][HTML] A hybrid long-term industrial electrical load forecasting model using optimized ANFIS with gene expression programming

MS Bakare, A Abdulkarim, AN Shuaibu, MM Muhamad - Energy Reports, 2024 - Elsevier
Electric energy demand forecasting is vital in contemporary power systems, especially
amidst market deregulation trends and the increasing influence of industrial customers on …

A Deep Learning-Driven Self-Conscious Distributed Cyber-Physical System for Renewable Energy Communities

G Cicceri, G Tricomi, L D'Agati, F Longo, G Merlino… - Sensors, 2023 - mdpi.com
The Internet of Things (IoT) is transforming various domains, including smart energy
management, by enabling the integration of complex digital and physical components in …

Anomalies and major cluster-based grouping of electricity users for improving the forecasting performance of deep learning models

K Aurangzeb - Frontiers in Energy Research, 2023 - frontiersin.org
Analyzing and understanding the electricity consumption of end users, especially the
anomalies (outliers), are vital for the planning, operation, and management of the power …

DBSCAN-based energy users clustering for performance enhancement of deep learning model

K Aurangzeb - Journal of Intelligent & Fuzzy Systems, 2024 - content.iospress.com
Background: Due to rapid progress in the fields of artificial intelligence, machine learning
and deep learning, the power grids are transforming into Smart Grids (SG) which are …

Load Forecasting with Hybrid Deep Learning Model for Efficient Power System Management

S Gochhait, DK Sharma… - Recent Advances in …, 2024 - ingentaconnect.com
Aim: Load forecasting for efficient power system management. Background: Short-term
energy load forecasting (STELF) is a valuable tool for utility companies and energy …

Mining temporal patterns to discover inter-appliance associations using smart meter data

S Osama, M Alfonse, AB M. Salem - Big Data and Cognitive Computing, 2019 - mdpi.com
With the emergence of the smart grid environment, smart meters are considered one of the
main key enablers for developing energy management solutions in residential home …

[PDF][PDF] Energy Reports

MS Bakare, A Abdulkarim, AN Shuaibu - researchgate.net
Electric energy demand forecasting is vital in contemporary power systems, especially
amidst market deregulation trends and the increasing influence of industrial customers on …

Analyse des courbes de charge d'électricité et prédiction à court terme dans les secteurs résidentiel et tertiaire

F Fahs - 2023 - theses.hal.science
Le déploiement massif des compteurs intelligents dans le secteur résidentiel et tertiaire a
permis de récolter des données de consommation électrique de haute fréquence à l'échelle …