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Machine Parameter Correction of Heat Load Model Based on Particle Swarm Optimization

Accurate prediction of the thermal load is critical for heat supply system control. A thermal load forecasting algorithm employing physical models and particle swarm optimization is proposed in this paper. The thermal model was derived by establishing the energy conservation equation based on physics laws. The model parameters were tuned by particle swarm optimization with operational data. The proposed algorithm was tested with actual two-week heating data. The test results demonstrate the proposed algorithm achieves an accurate and stable prediction of thermal load, and abnormal data and accumulation of forecasting errors over time were not observed.