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A Common Trap and Countermeasures in Application of Machine Learning in Energy Engineering
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  • Hao Chen,
  • Chao Zhang,
  • Yu Wang,
  • Ninghong Jia,
  • Xiliang Liu,
  • Ian Duncan,
  • Shenglai Yang
Hao Chen

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Chao Zhang
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Ninghong Jia
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Xiliang Liu
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Ian Duncan
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Shenglai Yang
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Machine learning (ML) algorithms have gained more and more successful applications in the field of energy prediction. However, current conventional application process of ML algorithms lacks screening of dominant factors and model validation, resulting in weakening the predictive ability of the model. In this work, systematic and robust predictive models are provided to address this issue. Based on 147 sets of data, various methods were used to predict. The results show that in the process of learning curve analysis, the eight models can get satisfactory results, but there are three models with overfitting or underfitting. Besides, through analyze the model by the single-factor control variables method, two additional defective models were found. Therefore, current conventional ML modeling methods are not reliable. This paper addresses the main reasons for the poor performance of some predictive models built by ML and provides guidelines on how to build robust predictive models.