Digital Twin-Driven Low-Carbon Service Design and Modularization in Central Air Conditioning Ecosystems: A Multi-Criteria and Co-Intelligence Approach

Abstract

The urgent global mandate for carbon neutrality necessitates a shift from traditional product-centric models towards Digital Twin (DT)-driven low-carbon service solutions, particularly in Central Air Conditioning (CAC) systems. This paper proposes a novel DT-driven framework for systematic low-carbon service design and modularization in CAC ecosystems. The framework first facilitates a comprehensive demand analysis, informed by a three-dimensional Energy Scenario Intelligence model and quantified using robust multi-criteria methods. The framework then introduces a novel methodology for the quantitative analysis of co-intelligence relationships, which provides the foundation for an advanced service module generation and optimization approach that leverages an improved Girvan Newman algorithm and Interval Type-2 Fuzzy TOPSIS to handle high-level uncertainties. A key contribution is the explicit elucidation of DT’s pivotal role in enabling predictive and systemic low-carbon capabilities. The framework’s effectiveness was verified in an intelligent office building, achieving a 74.29% integrated energy saving rate and an annual carbon reduction of 618.5 tCO2. The findings offer valuable theoretical insights and a practical methodology for designing and implementing sustainable CAC service ecosystems.