Electromagnetic emission measurement-based recognition of household appliances using artificial intelligence techniques

This paper presents a novel and non-intrusive approach for household appliance recognition based on electromagnetic interference (EMI) emission signatures using artificial intelligence techniques. Unlike conventional Non-Intrusive Load Monitoring (NILM) methods that rely on electrical parameters such as current and voltage, the proposed method utilizes radiated electromagnetic signals as distinctive features for appliance identification. The experimental setup employs a Gigahertz Transverse Electromagnetic (GTEM) cell to ensure a controlled and interference-free measurement environment, where EMI signals from six different household appliances are acquired and processed. A wavelet-based feature extraction method is applied to capture the transient characteristics of the EMI signals, followed by classification using Artificial Neural Networks (ANN) and Support Vector Machines (SVM) models. The ANN model achieved a high recognition accuracy of 97.7% using hold-out validation. To further assess robustness, a 10-fold cross-validation was conducted, resulting in an average accuracy of 94.5%, with a Kappa coefficient of 0.934, indicating excellent generalization capability. In comparison, the SVM classifier achieved an accuracy of 84.0% (hold-out) and 82.8% under cross-validation, with a Kappa coefficient of 0.794, demonstrating good performance but reduced effectiveness for appliances with nonlinear emission characteristics. The results also show that electronic appliances are classified with near-perfect accuracy, whereas minor confusion occurs among motor-based devices due to similarities in their emission patterns. The findings confirm that the integration of wavelet-based feature extraction with AI-driven classification offers an accurate and reliable solution for appliance identification based on electromagnetic emission patterns. This approach provides a valuable foundation for intelligent load monitoring, energy management, and appliance recognition systems.