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Research On Public Building Energy Consumption Prediction Method Based On NAR Neural Network Prediction Technology

机译：基于NAR神经网络预测技术的公共建筑能耗预测方法研究。

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摘要

In order to solve the problem of high energy consumption of public buildings and optimize and improve energy conservation of public buildings, we built a building energy consumption prediction model based on NAR neural network prediction technology improved by BP neural network algorithm, and the energy consumption value is predicted. The large public buildings as the research object, the key factors to determine the effect of building energy consumption and collect the corresponding data processing, as the input parameters of neural network prediction public buildings energy consumption value, according to the actual situation will eventually NAR prediction of neural network and BP network prediction method and the comparative analysis the measured data. The results show that NAR neural network can predict the energy consumption of public buildings more accurately than BP neural network under different building parameters.
机译：为了解决公共建筑能耗高的问题，优化和改善公共建筑节能，我们建立了基于BP神经网络算法改进的NAR神经网络预测技术的建筑能耗预测模型。被预测。以大型公共建筑为研究对象，决定建筑能耗影响的关键因素并收集相应的数据处理，作为神经网络的输入参数预测公共建筑能耗值，根据实际情况将最终进行NAR预测神经网络和BP网络预测方法的比较以及对实测数据的比较分析。结果表明，在不同建筑物参数下，NAR神经网络比BP神经网络能更准确地预测公共建筑的能耗。

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