INTELLIGENT HEAT SOURCE SELECTION UNDER STREAMING DATA QUALITY AND OPERATIONAL RISK CONSTRAINTS

This paper proposes a quality-aware decision model for selecting heat sources in an integrated thermal-energy system. The method combines technical, economic and environmental utility with the quality of streaming measurements and an FMEA-based operational-risk penalty. A source is selected only after critical data-quality constraints are satisfied; otherwise, the algorithm requests redundant confirmation or activates a validated safe mode. A reproducible computational experiment with five heat-source alternatives and five disturbance scenarios compares the proposed method with a quality-blind baseline. Under normal conditions both methods select the same source. Under delayed, drifting, lost and combined data disturbances, the proposed model rejects the unreliable nominal optimum and selects an alternative with verifiable input data. The model therefore links data-quality assessment directly to an explainable operational decision rather than treating it as a separate monitoring task.