Asad, Muhammad
ORCID: https://orcid.org/0009-0009-9746-9203
(2025)
Feature-Centric Approaches to Non-Intrusive Load Monitoring and Appliance Identification.
Masters thesis, Concordia University.
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Abstract
Load disaggregation refers to estimating appliance-level consumption from overall household energy data. It includes tasks like load identification and energy disaggregation. Researchers are actively developing various machine learning and deep learning techniques to disaggregate total household energy consumption into appliance-level usage. At the same time, many are focusing on identifying individual appliance loads to detect faulty devices or to improve the overall disaggregation process. This thesis makes two significant contributions to the field, addressing the challenges of total load separation and appliance identification. The first contribution focuses on energy disaggregation using a simplified Feed-Forward Neural Network architecture optimized for performance and efficiency. Oversampling techniques are developed for training data to improve the detection of
appliance activation cycles. Furthermore, the model incorporates additional features derived from aggregate consumption profiles, enhancing input diversity and robustness. This approach is tested on the RAE, REFIT, and REDD datasets under both clean and noisy conditions. The second contribution addresses appliance-level load identification using a Kolmogorov–Arnold Network, offering a lightweight and efficient alternative to deep models. Around 75 features are extracted from voltage and current signals, grouped into statistical, power-related, and frequency-domain categories. An effective feature selection process is conducted using multiple tests and correlation matrices to retain only the most informative inputs, thereby reducing model complexity and enhancing generalization. Additionally, we tune the hyperparameters of the KAN to control the degree of oversampling, allowing it to better handle imbalanced data. The model is evaluated using three public datasets: COOLL, PLAID, and WHITED.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Asad, Muhammad |
| Institution: | Concordia University |
| Degree Name: | M.A. Sc. |
| Program: | Quality Systems Engineering |
| Date: | 26 August 2025 |
| Thesis Supervisor(s): | Bouguila, Nizar and Amayri, Manar |
| Keywords: | Non-intrusive load Identification Kolmogorov-Arnold Network (KAN) Feature Importance Feature Extraction Load Identification Non-intrusive load monitoring (NILM) Feed-Forward Neural Network (FFNN) Oversampling Active power Denoising Auto-Encoder (DAE) |
| ID Code: | 996107 |
| Deposited By: | Muhammad Asad |
| Deposited On: | 04 Nov 2025 17:38 |
| Last Modified: | 04 Nov 2025 17:38 |
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