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This review examines the integration of artificial intelligence (AI) and machine learning (ML) in detecting user activities and identifying energy habits within Home Energy Management Systems (HEMSs). It highlights the effectiveness of deep learning models in achieving high accuracy for occupancy detection and load forecasting, while addressing challenges such as generalization and interpretability. The review calls for future research to focus on lightweight, explainable models and the integration of advan

Author

phuonggann2206

Language

EN