인공지능 기반 콜드체인 배송차량 에너지 소비량 예측

Abstract

Purpose : This study proposes a framework for a cold-chain logistics control system, and presents a method for extracting and utilizing data collected in real-time. Besides, an AI-based data learning used to predict energy consumed by cold-chain delivery vehicles is described with a case study. Research design, data and methodology : In this paper, the energy consumption of cold-chain delivery was predicted using Long Short-Term Memory (LSTM) to enhance competitiveness and efficient operations in a cold-chain logistics environment. Data on dairy product delivery were used in a case study. In total, 539 sets of data were acquired by collecting data every five minutes from five vehicles within 2,695 minutes. Results : The LSTM model seemed to fit actual energy usage data as the epochs of the model increased. Hence, the model is expected to be more effective in predicting energy consumption at epoch settings of 1,000 or more. The validation losses for each 100, 1,000, and 10,000 epochs were 0.72802, 0.01571, and 0.00546, respectively. Conclusions : In this study, the framework of an integrated management system for smart logistics centers was proposed. In addition, a method for extracting and utilizing data collected in real-time was presented. In particular, this study contributes to maintaining temperature and enhancing energy efficiency by predicting the energy consumption of cold-chain delivery vehicles through AI-based data learning.