期刊
ENERGY
卷 102, 期 -, 页码 660-668出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2016.02.134
关键词
Office building; Energy consumption prediction; VRV system; Data mining
资金
- Daikin (China) Investment Co., Ltd, Shanghai, China
- Qingdao Hisense-Hitachi Air-Conditioning Systems Co.,Ltd., Qingdao,Shandong Province,China
Energy consumption prediction plays an important role in building design & retrofit, energy management system. In this paper, ECI (Energy consumption intensity) of VRV (Variable refrigerant volume) system in office buildings located in Shanghai, Nanjing and Changsha of East China are firstly investigated and analyzed statistically. The annual median value of ECI in the three cities is about 36 kWh/m(2).Hourly energy consumption prediction model based on data mining (ie. ANN (Artificial Neural Network), SVM (Support vector regression) and ARIMA (Autoregressive integrated moving average) models) are subsequently discussed. During case study, three months' (ie. from July to September in 2013) energy consumption data of office building located in Shanghai are used to set up different predicting models. RMSE (Root mean square error), MSE (Mean square error) & MAPE (Mean absolute percentage error) are used to evaluate corresponding prediction accuracy. Results show that Predicting model based on ANN is better than the other two models'. The RMSE, MSE & MAPE of ANN model in training course are 0.0681, 0.0045, 0.1710, respectively. According to simulated results in paper, ANN and SVM models are recommended to do energy consumption predicting of VRV system in office buildings. (C) 2016 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据