| 摘要(英) |
In this paper, we use D company’s large-scale Automated storage system (Automated Storage / Retrieval System, AS/RS) warehouse in Taoyuan City Yangmei District as example, propose a deep learning method for temperature estimation. The method is based on Long Short-Term Memory (LSTM) model and Recurrent Neural Network (RNN) by using 7-days temperature mapping records. Using LSTM model with prior 150 hours key spots’ EDLM（Electronic data logging monitor）temperature data to predict future 18 hours temperature, then using RNN model with LSTM key spots’ predict data to estimate future 18 hours temperature of multiple spots in AS/RS. To validate the estimation result, we use the Real-time sensor temperature record collected in Building Management System (BMS) as validation data. Comparing the Max AE (Max Absolute Error) of LSTM and RNN model.
According to the result, no matter checking Models’ Max AE or RMSE, the methods all comply RMSE less than 0.20 and Max AE less than MPE (Maximum Permissible Error) which is 0.50℃. That also proves the feasibility of using real-time sensors as temperature mapping method, to replace large quantity of EDLM in Pharmaceutical warehouse. At the end of this paper, we describe some observations and potential variation, as the reference information for future studies. |
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