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학술지 Real-Time Cooling Load Forecasting Using a Hierarchical Multi-Class SVDD
Cited 7 time in scopus Download 3 time Share share facebook twitter linkedin kakaostory
저자
유재학, 이병복, 박대헌
발행일
201407
출처
Multimedia Tools and Applications, v.71 no.1, pp.293-307
ISSN
1380-7501
출판사
Springer
DOI
https://dx.doi.org/10.1007/s11042-013-1412-1
협약과제
12MC2500, 차세대 USN기반의 스마트 사회안전 프레임워크 기술 개발, 이병복
초록
In this paper, we propose a real-time cooling load forecasting system in order to overcome the problems of the conventional methods. The proposed system is a new load forecasting model that hierarchically combines Support Vector Data Descriptions (SVDDs). The proposed system selects an optimal attribute subset by our cooling load forecasting system that enables real-time load data generation and collection. The system is composed of two layers: The first layer predicts the time slots in three representative forms: morning, midday and afternoon. The second layer performs specialized prediction of each individual time slot. Since the proposed system enables both coarse-and fine-grained forecasting, it can efficient cooling load management. Moreover, even when a new time slot emerges, it can be easily adapted for incremental updating and scaling. The performance of the proposed system is validated via experiments which confirm that the recall and precision measures of the method are satisfactory. © 2013 Springer Science+Business Media New York.
키워드
Cooling load forecasting, Real-time prediction, Support vector data description, Support vector machine
KSP 제안 키워드
Conventional methods, Cooling load, Data generation, Efficient cooling, First layer, Incremental updating, Load data, Load forecasting, Real-Time Prediction, Support Vector Data Description, Support VectorMachine(SVM)