Occupancy Forecasting using LSTM Neural Network and Transfer Learning
Occupancy Forecasting using LSTM Neural Network and Transfer Learning
复制标题
使用 LSTM 神经网络和迁移学习进行占用率预测
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
W. Pora
中科院分区：
文献类型：
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作者：
Piyapat Leeraksakiat;W. Pora
神经网络可以在医学、农业和娱乐等多个领域用作预测工具。可以利用对诸如人的进入/离开行为的人类习惯的准确预测来控制电器，以便在保持舒适的同时减少能量消耗。然而，神经网络有一个问题，那就是它只能被训练来预测一个人的行为。如果神经网络被用来预测另一个人，它会降低准确性。虽然将收集新数据以重新训练神经网络，但数据收集可能需要很长时间。本文提出在长短期记忆（LSTM）网络上使用迁移学习，以便在特定人员使用房间、人员改变其行为或新人占用房间后提高网络的性能。在网络由范数数据集训练之后，可以应用新的采样数据批次来更新网络，换句话说，在现有知识的基础上传输新知识。结果表明，迁移学习有助于LSTM网络能够持续跟踪行为变化。它的预测变得越来越准确，当相比，规范之一。
Neural networks can be used as a forecasting tool in several fields such as medicine, agriculture, and entertainment. Accurate forecasting of human habit such as the entry/exit behavior of a person may be exploited to control electrical appliances in order to reduce energy consumption while maintaining comfort. However, the neural network has a problem that is it can be trained to forecast behavior of only one person. If the neural network is used to predict another person, It will decrease accuracy. Although new data will be collected to re-train the neural network, data collection might take long time. This paper proposes to use transfer learning on a Long Short-Term Memory (LSTM) network in order to improve the performance of the network after a specific person uses the room, the person changes his/her behavior, or a new person occupies the room. After a network is trained by a norm dataset, then new batches of sampling data can be applied to update the network, in other words, to transfer the new knowledge on top of the existing one. The results show that transfer learning helps the LSTM network to be able to track the behavior change continually. Its forecast becomes more and more accurate, when compared to that of the norm one.