Sustainable Artificial Intelligence for Renewable Smart Home Automation

Abstract

This paper develops a focused conceptual framework for Sustainable Artificial Intelligence (SAI) in renewable smart home automation. It shows how artificial intelligence, Internet of Things devices, and household renewable energy systems can be coordinated to improve energy efficiency while preserving user comfort. The study adopts a conceptual framework methodology by synthesizing literature on smart home energy management, IoT interoperability, edge/cloud AI, user adoption, and behavioral energy feedback. This synthesis is translated into a layered architecture and illustrative household scenarios. The framework identifies three design principles: contextual awareness, energy-aware optimization, and explainable user guidance. These principles are supported by four system layers: physical IoT devices, renewable energy assets, application/network services, and AI decision logic. As a conceptual study, the paper does not claim experimental validation; instead, it provides a structured basis for future simulation, pilot deployment, and longitudinal user studies across diverse housing and energy contexts. The paper contributes by narrowing sustainable AI from a general idea into a practical smart home framework that integrates technical, behavioral, and renewable energy considerations.