ENHANCING SMART HOME SELF-REGULATION THROUGH THE INTEGRATION OF RULE-BASED SYSTEMS, EFFICIENT OPTIMIZATION ALGORITHMS AND PREDICTIVE MODELLING ANALYSIS USING HOURLY AND DAILY DATA SEGMENTATION FOR ACTIVITY DETECTION

Abstract

Abstract: - The proliferation of smart home technologies has opened new opportunities for enhancing user comfort, convenience, and energy efficiency. However, the effectiveness of smart home automation heavily relies on understanding and adapting to user routines and behaviours. This paper presents a novel approach to smart home self-regulation by leveraging user routine analysis and adaptive control models. We propose a framework that combines pattern mining techniques, machine learning (ML) algorithms, and rule-based systems to analyse user routines from smart home datasets and develop personalized control strategies. Our methodology involves three key stages: (1) mining frequent device usage patterns and understanding user behaviour; (2) implementing a rule-based system for intelligent decision-making and device automation. (3) implementing optimization Algorithms and predictive modelling. We evaluate our approach using real-world smart home dataset, representing a user's device usage patterns. The experimental results demonstrate the effectiveness of our framework in capturing user routines, adapting to behavioural changes, and providing personalized automation recommendations. We also conduct a comparative analysis with existing approaches and discuss the benefits of our adaptive control models in terms of energy savings, user satisfaction, and system performance. This research contributes to the advancement of smart home automation by showcasing the importance of user-centric approaches and the potential of self-regulating systems. The proposed framework has practical implications for the development of intelligent smart home systems that can dynamically adapt to user needs and preferences, ultimately leading to improved user experiences and energy efficiency.