改进鲸鱼算法构建混合模型的建筑能耗预测.
Building energy consumption data has non-stationary and nonlinear characteristics. A single prediction model is difficult to predict accurately, and a new hybrid model for short-term prediction of building energy consumption is proposed. Complementary ensemble empirical mode decomposition (CEEMD) is...
| Publicado en: | Studia Poliana no. 22; pp. 197 - 203 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Studia Poliana
2020
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=142451847&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 142451847 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 11396660 8XIR jtl: Studia Poliana issn: 11396660 maglogo: N pubinfo: dt: 2020 iid: 22 pid: 94698 pub: Studia Poliana artinfo: ui: 142451847 10.16526/j.cnki.11—4762/tp.2020.02.041 ppf: 197 ppct: 6 formats: fmt: @attributes: type: P size: 5.1MB tig: atl: 改进鲸鱼算法构建混合模型的建筑能耗预测. aug: au: 王茹 宋爽 贺佳 affil: 西安建筑科技大学信息与控制工程学院,西安 710055 su: Energy consumption of buildings Hilbert-Huang transform Mathematical optimization Forecasting Differential evolution Shanghai (China) sug: subj: Shanghai (China) Energy consumption of buildings Hilbert-Huang transform Mathematical optimization Forecasting Differential evolution keyword: building energy consumption complementary ensemble empirical mode decomposition prediction upgraded whale optimization algorithm 互补集合经验模态分解 建筑能耗 改进的鲸鱼优化算法 预测 ab: Building energy consumption data has non-stationary and nonlinear characteristics. A single prediction model is difficult to predict accurately, and a new hybrid model for short-term prediction of building energy consumption is proposed. Complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose volatility energy data into a set of intrinsic mode functions and a residual sequence. Based on reverse learning, differential evolution algorithm and control parameters, the Whale Optimization Algorithm (WOA) is upgraded to effectively solve the problem of premature convergence and local optimality, and the upgraded whale optimization algorithm (UWOA) is proposed. UWOA is utilized to optimize the weights and thresholds of the Elman neural network. The optimized Elman neural network predicts and integrates the intrinsic mode functions and the residual sequence, and then the energy prediction is obtained. The CEEMD-UWOA-Elman hybrid model is used to predict the energy consumption of a large public building in Shanghai. The results show that the hybrid model has a good prediction effect. 建筑能耗数据具有非平稳和非线性特征,单一预测模型很难对其进行精准预测,提出一种用于建筑能耗短期预测的新型混合模型。利用互补集合经验模态分解方法(CEEMD)将波动性较大的能耗数据分解为一组本征模态函数和一个残差序列；基于反向学习、差分进化算法并引入控制参数对鲸鱼优化算法(WOA)进行改进,有效解决算法早熟收敛与陷入局部最优等问题,提出改进算法UWOA(upgraded whale optimization algorithm)；利用UWOA优化Elman神经网络的权值与阈值,优化后的Elman神经网络对本征模态函数和残差序列进行预测并集成,得到能耗预测值。应用CEEMD-UWOA-Elman混合模型对上海某大型公共建筑能耗进行短期预测,结果显示混合模型获得很好的预测效果。 pubtype: Academic Journal doctype: Article src: R language: Chinese refInfo: copyright: @attributes: flag: Y custom: Copyright of Studia Poliana is the property of Studia Poliana and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Studia Poliana holder: Studia Poliana dt: @attributes: year: 2020 holdings: @attributes: islocal: N |
|---|