Real-time cooling load forecasting using a hierarchical multi-class SVDD

Abstract

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.