Cascade-based short-term forecasting method of the electric demand of HVAC system

被引：10
|
作者：
Le Cam, M. [1 ]
Zmeureanu, R. [1 ]
Daoud, A. [2 ]
机构：
[1] Concordia Univ, Dept Bldg Civil & Environm Engn, Ctr Zero Energy Bldg Studies, 1515 St Catherine W, Montreal, PQ H3G 1M8, Canada
[2] Inst Rech Hydro Quebec, Lab Technol Energie, 600 Ave Montagne, Shawinigan, PQ G9N 7N5, Canada
来源：
ENERGY | 2017年 / 119卷
基金：
加拿大自然科学与工程研究理事会;
关键词：
Multistep forecasting; Demand response; Data mining; Measurements; HVAC system; LOAD; SELECTION;
D O I：
10.1016/j.energy.2016.11.064
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
This paper presents a multi-step-ahead forecasting method of the electric demand in a large institutional building to be used in the context of demand response control strategy. A cascade-based method is proposed for electric demand forecasting of the cooling system over the next six hours with a time-step of 15 min. Data mining techniques are used for pre-processing the measurements and improving the forecasting models. Data-driven models are developed by using Building Automation System (BAS) trend data of an existing building. First, the air flow rate supplied by the Air Handling Units (AHUs) is forecasted, followed by the cooling coils load, and the whole building cooling load. Finally, the electric demand of the supply fans, chillers and cooling towers, and the total electric demand of the cooling system of the building are forecasted over six hours. The comparison of the forecasted electric demand of the cooling system for the existing building over the six-hour test and the measurements show good agreement with CV(RMSE) of 14.2-22.5%. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1098 / 1107
页数：10
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