BP Neural Network for the Prediction of Urban Building Energy Consumption Based on Matlab and its Application

Qu Shilin, Sun Zhifeng, Fan Huifang, L. Kun
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引用次数: 19

Abstract

Urban building energy consumption is one important part of the total social energy consumption. How to predict building energy consumption of urban development trends and master the changes of urban building energy consumption are important link of building energy-saving. In order to forecast urban building energy consumption from both the macro and micro aspects, multi-layer feed forward artificial neural network based on BP algorithm is proposed in the paper and used MATLAB implements to design and train of improving BP neural network. A macro urban building energy consumption prediction model and a micro urban building energy consumption prediction model are established in the paper, they are the city electricity demand prediction model and air-conditioning system energy consumption prediction model. The accuracy and feasibility of the prediction models were studied by calculation the difference between practical data and simulation model predictions.
基于Matlab的城市建筑能耗预测BP神经网络及其应用
城市建筑能耗是社会总能耗的重要组成部分。如何预测城市建筑能耗的发展趋势,掌握城市建筑能耗的变化是建筑节能的重要环节。为了从宏观和微观两方面对城市建筑能耗进行预测,本文提出了基于BP算法的多层前馈人工神经网络,并利用MATLAB实现对改进BP神经网络的设计和训练。本文分别建立了宏观城市建筑能耗预测模型和微观城市建筑能耗预测模型,即城市电力需求预测模型和空调系统能耗预测模型。通过计算实际数据与模拟模型预测值的差值,研究了预测模型的准确性和可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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