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 …
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 …
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
Electric energy demand forecasting is vital in contemporary power systems, especially
amidst market deregulation trends and the increasing influence of industrial customers on …
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
The Internet of Things (IoT) is transforming various domains, including smart energy
management, by enabling the integration of complex digital and physical components in …
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 …
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 …
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 …
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 …
main key enablers for developing energy management solutions in residential home …
[PDF][PDF] Energy Reports
Electric energy demand forecasting is vital in contemporary power systems, especially
amidst market deregulation trends and the increasing influence of industrial customers on …
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 …
permis de récolter des données de consommation électrique de haute fréquence à l'échelle …