Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- 5G (1)
- Agglomerative Hierarchical Clustering (1)
- Artificial Intelligence (1)
- Cybersecurity (1)
- Deep Learning (1)
-
- Demand Response (1)
- Distributed Learning (1)
- Dynamic Time Warping (1)
- Energy Management (1)
- Federated Learning (1)
- Intrusion Detection System (1)
- Load Curve Clustering (1)
- Load Forecasting (1)
- Online Learning (1)
- QoS Prediction (1)
- QoS metrics (1)
- Recurrent Neural Networks (1)
- Shape-Based Clustering (1)
- Time-Series analysis (1)
- Publication
- Publication Type
Articles 1 - 3 of 3
Full-Text Articles in Computer Engineering
Agglomerative Hierarchical Clustering With Dynamic Time Warping For Household Load Curve Clustering, Fadi Almahamid, Katarina Grolinger
Agglomerative Hierarchical Clustering With Dynamic Time Warping For Household Load Curve Clustering, Fadi Almahamid, Katarina Grolinger
Electrical and Computer Engineering Publications
Energy companies often implement various demand response (DR) programs to better match electricity demand and supply by offering the consumers incentives to reduce their demand during critical periods. Classifying clients according to their consumption patterns enables targeting specific groups of consumers for DR. Traditional clustering algorithms use standard distance measurement to find the distance between two points. The results produced by clustering algorithms such as K-means, K-medoids, and Gaussian Mixture Models depend on the clustering parameters or initial clusters. In contrast, our methodology uses a shape-based approach that combines Agglomerative Hierarchical Clustering (AHC) with Dynamic Time Warping (DTW) to classify …
Exploring Artificial Intelligence (Ai) Techniques For Forecasting Network Traffic: Network Qos And Security Perspectives, Ibrahim Mohammed Sayem
Exploring Artificial Intelligence (Ai) Techniques For Forecasting Network Traffic: Network Qos And Security Perspectives, Ibrahim Mohammed Sayem
Electronic Thesis and Dissertation Repository
This thesis identifies the research gaps in the field of network intrusion detection and network QoS prediction, and proposes novel solutions to address these challenges. Our first topic presents a novel network intrusion detection system using a stacking ensemble technique using UNSW-15 and CICIDS-2017 datasets. In contrast to earlier research, our proposed novel network intrusion detection techniques not only determine if the network traffic is benign or normal, but also reveal the type of assault in the flow. Our proposed stacking ensemble model provides a more effective detection capability than the existing works. Our proposed stacking ensemble technique can detect …
Deep Learning For Load Forecasting With Smart Meter Data: Online And Federated Learning, Mohammad Navid Fekri
Deep Learning For Load Forecasting With Smart Meter Data: Online And Federated Learning, Mohammad Navid Fekri
Electronic Thesis and Dissertation Repository
Electricity load forecasting has been attracting increasing attention because of its importance for energy management, infrastructure planning, and budgeting. In recent years, the proliferation of smart meters has created new opportunities for forecasting on the building and even individual household levels. Machine learning (ML) has achieved great successes in this domain; however, conventional ML techniques require data transfer to a centralized location for model training, therefore, increasing network traffic and exposing data to privacy and security risks. Also, traditional approaches employ offline learning, which means that they are only trained once and miss out on the possibility to learn from …