Impact of Derived Features from the Controlled Environment Agriculture Scenarios on Energy Consumption Prediction Model
Autor(en): |
Yifan Cao
Yangda Chen Mingwen Shi Chuanzhen Li Weijun Wu Yapeng Li Xuxin Guo Xianpeng Sun |
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Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Buildings, 13 Januar 2023, n. 1, v. 13 |
Seite(n): | 250 |
DOI: | 10.3390/buildings13010250 |
Abstrakt: |
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. |
Copyright: | © 2023 by the authors; licensee MDPI, Basel, Switzerland. |
Lizenz: | Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden. |
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10712742 - Veröffentlicht am:
21.03.2023 - Geändert am:
10.05.2023