关键词: 超高温热泵, 随机森林, 变量选择

Abstract:

In the context of decarbonizing industrial heat demand, ultra-high temperature heat pumps, serving as active thermal energy recovery systems, are emerging as pivotal technologies for energy conservation and emission reduction. These systems convert low-grade waste heat into high-grade thermal energy with minimal electrical energy consumption. While augmenting the number of heat exchangers and compressors and refining their layout have proven beneficial in boosting system performance, they inevitably introduce complexity, posing additional hurdles for system analysis and optimization. To address this, feature importance-based variable selection techniques offer an effective means of reducing data dimensionality and swiftly pinpointing crucial system components. However, traditional correlation analysis methods frequently falter in producing consistent results when data is missing. To overcome this limitation, this study introduces a novel method for quantifying feature importance using the random forest model. Analytical results reveal that the random forest approach demonstrates superior generalization abilities when applied to 100 datasets containing missing data, achieving a variance in feature importance quantification of 0.11505, notably lower than the 0.17055 variance attained with the correlation coefficient method. Moreover, the results indicate that coupling temperature is the primary determinant affecting system performance, thus identifying a key area for further optimizing system design. Additionally, the study finds that the influence of output temperature on system efficiency is less than 5%, suggesting the system's low sensitivity to variations in output temperature and emphasizing its potential for ultra-high temperature applications.

Key words: ultra-high temperature heat pump, random forest, variable selection