Identification Method for Resistance Coefficients in Heating Networks Based on an Improved Differential Evolution Algorithm

The intelligent upgrade of heating systems faces the challenge of accurately identifying high-dimensional pipe-network resistance coefficients; difficulties in accomplishing this can lead to hydraulic imbalance and redundant energy consumption. To address the limitations of traditional Differential Evolution (DE) algorithms under high-dimensional operating conditions, this paper proposes an Improved Differential Evolution Algorithm (SDEIA) incorporating chaotic mapping, adaptive mutation and crossover strategies, and an immune mechanism. Furthermore, a multi-constrained identification model is constructed based on Kirchhoff’s laws. Validation with actual engineering data demonstrates that the proposed method achieves a lower average relative error in resistance coefficients and exhibits a more concentrated error distribution. SDEIA provides a high-precision tool for multi-heat-source networking and dynamic regulation in heating systems, facilitating low-carbon and intelligent upgrades.