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首页> 外文会议>Annual IEEE International Systems Conference >Energy demand prediction through novel random neural network predictor for large non-domestic buildings
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Energy demand prediction through novel random neural network predictor for large non-domestic buildings

机译：通过新型随机神经网络预测器预测大型非住宅建筑的能源需求

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摘要

Buildings are among the largest consumers of energy in the world. In developed countries, buildings currently consumes 40% of the total energy and 51% of total electricity consumption. Energy prediction is a key factor in reducing energy wastage. This paper presents and evaluates a novel RNN technique which is capable to predict energy utilization for a non-domestic large building comprising of 562 rooms. Initially, a model for the 562 rooms is developed using Integrated Environment Solutions Virtual Environment (IES-VE) software. The IES-VE model is simulated for one year and 10 essential data inputs i.e., air temperature, dry resultant temperature, internal gain, heating set point, cooling set point, plant profile, relative humidity, moisture content, heating plant sensible load, internal gain and number of people are measured. Datasets are generated from the measured data. RNN model is trained with this datasets for the energy demand prediction. Experiments are used to identify the accuracy of prediction. The results show that the proposed RNN based energy model achieves 0.00001 Mean Square Error (MSE) in just 86 epochs via Gradient Decent (GD) algorithm.
机译：建筑物是世界上最大的能源消耗国之一。在发达国家，建筑物目前消耗的能源总量占总能源的40％，而电力消耗占总能源消耗的51％。能源预测是减少能源浪费的关键因素。本文介绍并评估了一种新颖的RNN技术，该技术能够预测由562个房间组成的非住宅大型建筑物的能源利用。最初，使用集成环境解决方案虚拟环境（IES-VE）软件开发了562个房间的模型。对IES-VE模型进行了一年和10个基本数据输入的仿真，这些数据输入包括空气温度，干燥的合成温度，内部增益，加热设定点，冷却设定点，设备配置文件，相对湿度，水分含量，供热设备的合理负荷，内部衡量人数和人数。数据集是根据测量数据生成的。使用该数据集训练RNN模型以进行能源需求预测。实验用于确定预测的准确性。结果表明，所提出的基于RNN的能量模型通过梯度体面（GD）算法仅在86个纪元内就实现了0.00001均方误差（MSE）。

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