Building Energy Prediction Using Artificial Neural Networks (LSTM)

Sankhanil Goswami
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引用次数: 1

Abstract

Modern buildings account for a significant proportion of global energy consumption worldwide. Therefore, accurate energy use forecast is necessary for energy management and conservation. With the advent of smart sensors, a large amount of accurate energy data is available. Also, with the advancements in data analytics and machine learning, there have been numerous studies on developing data-driven prediction models based on Artificial Neural Networks (ANNs). In this work a type of ANN called Large Short-Term Memory (LSTM) is used to predict the energy use and cooling load of an existing building. A university administrative building was chosen for its typical commercial environment. The network was trained with one year of data and was used to predict the energy consumption and cooling load of the following year. The mean absolute testing error for the energy consumption and the cooling load were 0.105 and 0.05. The percentage mean accuracy was found to be 92.8% and 96.1%. The process was applied to several other buildings in the university and similar results were obtained. This indicates the model can successfully predict the energy consumption and cooling load for the buildings studied. The further improvement and application of this technique for optimizing building performance are also explored.
基于人工神经网络的建筑能耗预测
现代建筑在全球能源消耗中占很大比例。因此,准确的能源使用预测是能源管理和节约的必要条件。随着智能传感器的出现,可以获得大量准确的能源数据。此外,随着数据分析和机器学习的进步,基于人工神经网络(ANNs)开发数据驱动预测模型的研究也越来越多。在这项工作中,一种称为大短期记忆(LSTM)的人工神经网络被用于预测现有建筑的能源使用和冷却负荷。选择了具有典型商业环境的大学行政大楼。该网络使用一年的数据进行训练,并用于预测下一年的能耗和冷负荷。能耗和冷负荷的平均绝对测试误差分别为0.105和0.05。平均准确率分别为92.8%和96.1%。该过程应用于大学的其他几座建筑,并获得了类似的结果。这表明该模型能够较好地预测所研究建筑的能耗和冷负荷。并对该技术在优化建筑性能方面的进一步改进和应用进行了探讨。
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