Artificial Intelligence–Driven Business Models for Smart Solar-Powered Energy Systems

Abstract

The accelerating transition towards sustainable energy has positioned smart solar-powered energy systems as a critical component of modern energy infrastructures. Alongside technological innovation, Artificial Intelligence (AI) is reshaping how value is created, delivered, and captured within the renewable energy sector through the emergence of AI-driven business models. This paper examines how AI-enabled business models enhance the efficiency, scalability, and economic viability of smart solar energy systems. It explores the integration of AI techniques such as machine learning, predictive analytics, and optimisation algorithms in core business functions including energy forecasting, asset management, dynamic pricing, customer engagement, and decentralised energy trading. By embedding intelligence into decision-making processes, firms are able to shift from traditional product-centric models towards data-driven, service-oriented, and platform-based models that prioritise performance, adaptability, and sustainability. The study further analyses how AI-driven insights enable proactive maintenance, demand–supply balancing, and personalised energy solutions, thereby reducing operational costs and improving system reliability. From a strategic perspective, the paper highlights the role of AI in facilitating new revenue streams, supporting peer-to-peer energy markets, and strengthening competitive advantage in increasingly complex energy ecosystems. It also discusses key challenges associated with data governance, algorithmic transparency, cybersecurity, and regulatory alignment. The paper concludes that AI-driven business models are not merely technological enhancements but transformative mechanisms that can accelerate the adoption of smart solar-powered energy systems while contributing to long-term economic resilience and environmental sustainability.