Republic of Turkey, one of the European Union candidate countries, is affected by social and economical, from Greenhouse gas emission (GHG). Therefore, like the other European countries, Republic of Turkey is also in the works to reduce GHG emission rate and make renewable energy sources such as Wind power generator, solar cell technologies, more efficient. In this study, classification of Turkey in terms of energy efficiency, energy import dependency, total renewable energy (TRE), GHG and energy consumption compared to other European Union countries is defined by using k- Means Clustering algorithm.

Non-Intrusive Load Monitoring (NILM) is an essential component of modern smart grid systems, enabling the disaggregation of total power consumption into appliance-level usage. Accurate appliance classification is critical for optimal power flow and uninterrupted grid operation. In state-of-the-art NILM applications, classical machine learning (ML) models require abundant labeled data. However, in real-world scenarios, limited data and measurement noise are common challenges. Quantum machine learning (QML), which leverages quantum mechanical properties, may offer advantages for NILM compared to classical ML. In this study, we propose QLID-Net (Quantum Load IDentification Network), a hybrid quantum-classical neural network for appliance classification in NILM. The proposed architecture consists of a classical encoder, a variational quantum circuit (VQC), and a classical decoder. We evaluated the model on a smart meter dataset (five appliance classes) across four experiments: data efficiency, classical/data noise robustness, baseline comparison, and quantum hardware noise robustness, comparing it against XGBoost, TabPFN, Random Forest, and a parameter-matched classical neural network. In the data efficiency experiment, the proposed model outperformed two classical models at 10 or fewer samples per class and significantly outperformed the parameter-matched classical neural network ( $p \lt 0.001$ , Cohen’s $d = 1.80$ ), with a crossover point at approximately 20 samples per class. Under additive noise (Gaussian, Uniform), the proposed model showed superior robustness, outperforming all classical baselines with up to 17.9% improvement over XGBoost and up to 21.3% improvement over TabPFN. In the baseline comparison, it achieved competitive performance with XGBoost and TabPFN (within 5.1%) and 100% training convergence compared to 70% for the classical neural network. Finally, in the quantum hardware noise tests, results showed that although QLID-Net can compete with the parameter-matched classical neural network, it faces a performance decline. Our results identify practical regimes where quantum advantages are beneficial, especially for scenarios with limited data or measurement noise. This study contributes to the growing evidence of practical QML applications and paves the way for quantum-assisted hybrid models in NILM.