基于傅里叶变换的多联机空调负荷 提取及预测方法
Load extraction and load forecasting method of VRF air conditioners based on Fourier transform
摘要：
多联机空调实际制冷量通常不等同于房间真实的空调负荷需求,导致基于数据驱动的负荷预测模型的预测效果往往较差。本文提出了一种基于傅里叶变换的从实际制冷量数据中提取空调真实负荷数据的方法。案例预测结果表明,该方法能够有效提取空调负荷数据,并能将基于人工神经网络的超短期空调负荷预测模型精度提升10.94%。
Abstract：
The actual cooling capacity of variable refrigerant flow (VRF) air conditioners is usually not equivalent to the real air conditioning load demand of room, resulting in the poor forecasting effect of data-driven load forecasting models. In this paper, a method based on Fourier transform is proposed to extract the real load of air conditioner from the actual cooling capacity. The case forecasting results show that the proposed method can effectively extract the air conditioning load data and improve the accuracy of the ultra-short-term air conditioning load forecasting models based on artificial neural networks by 10.94%.
Keywords:variable refrigerant flow (VRF) air conditioner; cooling capacity; load forecasting; data driving; Fourier transform