云计算助力智慧建筑节能减排与管理控制研究

王振峰 徐明霞

摘 要：由于近年来智慧城市受到了广泛的关注，而对于智慧城市节能减排还并没有更多的研究资料，所以本文中对于智慧城市的节能减排及其管理进行了分析。本文中主要进行研究的是BP神经网络对于智慧城市基础之上大型建筑的耗电量预测，通过预测耗电量可以分析得出建筑的能耗是否合理，以便做出进一步的管理动作。首先本文通过使用云计算的方法对整体的数据处理系统进行了规定，整体数据通过云计算的方式进行存储与计算。随后规定了文章中所使用的BP神经网络模型，通过BP神经网络的方法建立对于大型建筑的能耗预测模型，并且将所获取的数据用来进行模型的训练以及测试。模型的训练结果显示，经过125次迭代之后，整体模型的误差值达到最小。模型测试结果显示出，本文模型可以很好的进行能耗的预测，整体预测趋势与实际检测值基本一致。所以本文所设计模型能够达到跟踪建筑能耗的目的，能够为智慧城市节能减排以及其管理控制提供一定的理论基础。

关键词：BP神经网络;云计算;智慧建筑;节能环保

中图分类号：TU201.5;TP391.44  文献标识码：A     文章编号：1001-5922（2021）10-0090-04

Energy Conservation and Emission Reduction and Its Management Control of Smart Buildings by Using Cloud Computing

Wang Zhenfeng， Xu Mingxia

（Shaanxi Institute of Technology， Xi an710300， China ）

Abstract：In recent years， smart city has attracted extensive attention， but there are few research data on energy conservation and emission reduction of smart city， so energy conservation and emission reduction and its management of smart city are analyzed. Back propagation （BP） neural network is used to predict the power consumption of large buildings in smart city. By predicting the power consumption， whether the energy consumption of buildings is reasonable can be assessed， so as to make further management. First， the overall data processing system is defined by using cloud computing， and the overall data are stored and calculated by cloud computing. Then， the BP neural network model is specified to establish an energy consumption prediction model of large buildings， and the obtained data are adopted for training and testing of the model. The training results of the model indicate that after 125 iterations， the error of the model reaches its minimum value. The test results of the model suggest that the model can predict energy consumption accurately， and the overall prediction trend is consistent with the actual detected value. Thus， the model designed in this paper can achieve the purpose of tracking building energy consumption， and can provide a certain theoretical basis for smart city energy saving and emission reduction as well as its management and control.

Key words：BP neural network; cloud computing; Intelligent building;energy conservation and environmental protection

0 引言

近年來，智慧城市的概念受到了广泛的支持，智慧城市引起了科学界和工业界的广泛关注和兴趣，世界各地涌现出越来越多的国际案例[1-2]。然而，尽管智慧城市在应对近期的城市相关问题时可以发挥重要作用，但由于无法充分发挥其潜力以及被大量厂商炒作而受到批评[3]。……