Building Energy Consumption Prediction: An Extreme Deep Learning Approach
文献类型：期刊论文
| 作者 | Li, Chengdong1; Ding, Zixiang1 ; Zhao, Dongbin2; Yi, Jianqiang2; Zhang, Guiqing1
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| 刊名 | ENERGIES
 |
| 出版日期 | 2017-10-01 |
| 卷号 | 10期号:10页码:1-20 |
| 关键词 | Building Energy Consumption Deep Learning Stacked Autoencoders Extreme Learning Machine |
| DOI | 10.3390/en10101525 |
| 文献子类 | Article |
| 英文摘要 | Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the building energy consumption. In order to obtain better building energy consumption prediction accuracy, an extreme deep learning approach is presented in this paper. The proposed approach combines stacked autoencoders (SAEs) with the extreme learning machine (ELM) to take advantage of their respective characteristics. In this proposed approach, the SAE is used to extract the building energy consumption features, while the ELM is utilized as a predictor to obtain accurate prediction results. To determine the input variables of the extreme deep learning model, the partial autocorrelation analysis method is adopted. Additionally, in order to examine the performances of the proposed approach, it is compared with some popular machine learning methods, such as the backward propagation neural network (BPNN), support vector regression (SVR), the generalized radial basis function neural network (GRBFNN) and multiple linear regression (MLR). Experimental results demonstrate that the proposed method has the best prediction performance in different cases of the building energy consumption. |
| WOS关键词 | MULTIPLE LINEAR-REGRESSION ; ARTIFICIAL NEURAL-NETWORKS ; AUTOCORRELATION FUNCTION ; ELECTRICITY CONSUMPTION ; MACHINE ; MODELS |
| WOS研究方向 | Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:000414578400080 |
| 资助机构 | National Natural Science Foundation of China(61473176 ; Natural Science Foundation of Shandong Province for Young Talents in Provincial Universities(ZR2015JL021) ; 61105077 ; 61573225) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/19311]  |
| 专题 | 复杂系统管理与控制国家重点实验室_深度强化学习 |
| 作者单位 | 1.Shandong Jianzhu Univ, Sch Informat & Elect Engn, Jinan 250101, Shandong, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Chengdong,Ding, Zixiang,Zhao, Dongbin,et al. Building Energy Consumption Prediction: An Extreme Deep Learning Approach[J]. ENERGIES,2017,10(10):1-20. |
| APA | Li, Chengdong,Ding, Zixiang,Zhao, Dongbin,Yi, Jianqiang,&Zhang, Guiqing.(2017).Building Energy Consumption Prediction: An Extreme Deep Learning Approach.ENERGIES,10(10),1-20. |
| MLA | Li, Chengdong,et al."Building Energy Consumption Prediction: An Extreme Deep Learning Approach".ENERGIES 10.10(2017):1-20. |
入库方式： OAI收割
来源：自动化研究所
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