基于机器学习的一体式电制热储热装置能效测试方法OA

Under the dual carbon target,electric heating technology has achieved large-scale applications by virtue of its cleanliness and flexibility.Accurate detection and evaluation for the electric heating systems integrated heat storage device is the key to the efficient use of this technology.However,existing test methods are unsuitable for the detecting the systems due to instantaneous state instability and periodic fluctuations of water-supply temperature and electric power.An energy efficiency evaluation method suitable for electric heating-heat storage integrated systems is proposed,which is comprised of three processes:experimental preparation stage,pre-experimentation stage and experimental testing stage.Based on the monitoring data from the experimental testing stage,the energy efficiency evaluation executed on the electric heating device under whole-process and all-scenario steady states is realized by the machine learning model.The simulation results show that the proposed method can reflect the energy efficiency of the integrated system accurately,with an evaluation accuracy rate 5.74 percentage points higher than that obtained by the detection method specified in the national standard.And the proposed method can reduce the negative influence of transient instability and periodic fluctuations of water-supply temperature and electric power on the evaluation test for the integrated system,and can be applied to other electric heating devices for energy efficiency evaluation.