Impact of Derived Features from the Controlled Environment Agriculture Scenarios on Energy Consumption Prediction Model

The high energy consumption CEA building brings challenges to the management of the energy system. An accurate energy consumption prediction model is necessary. Although there are various prediction methods, the prediction method for the particularity of CEA buildings is still a gap. This study proposes some derived features based on the CEA scenarios to improve the accuracy of the model. The study mainly extracts the time series and logical features from the agricultural calendar, the botanical physiological state, building characteristics, and production management. The time series and logical features have the highest increase of 2.8% and 3.6%, respectively. In addition, four automatic feature construction methods are also used to achieve varying degrees of influence from −9% to 8%. Therefore, the multiple feature extraction and feature construction methods proposed in this paper can effectively improve the model performance.

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