Applying deep learning-based regional feature recognition from macro-scale image to assist energy saving and emission reduction in industrial energy systems

By integrating advanced deep learning and specific energy domain knowledge, Chen et al. develop a novel deep learning-based image recognition to assist energy saving and emission reduction for industrial energy systems, which greatly increases the recognition accuracy by 18.3% and decreases the energy consumption by 34.5%. 1 These authors contributed equally to this work. • A novel energy knowledge-integrated image recognition technology was applied to assist energy savings. • Non-physical image information was combined with specific energy domain knowledge for the first time. • The proposed image recognition model enables both high recognition accuracy and strong generalization ability. • The energy consumption reduces by 34.5% through adopting image recognition-based control optimization method. • The proposed control optimization method can be easily extended to general energy systems. Intro duction Image recognition technology has immense potential to be applied in industrial energy systems for energy conservation. However, the low recognition accuracy and generalization ability under actual operation conditions limit its commercial application. To improve the recognition accuracy and generalization ability, a novel image recognition method integrating deep learning and domain knowledge was applied to assist energy saving and emission reduction for industrial energy systems. As a typical industrial scenario, the defrosting control in the refrigeration system was selected as the specific optimization object. By combining deep learning algorithm with domain knowledge, a residual-based convolutional neural network model (RCNN) was proposed specifically for frosty state recognition, which features the residual input and average pooling output. Based on the real-time recognition of frosty levels, a defrosting control optimization method was proposed to initiate and terminate the defrosting operation on demand. By combining the advanced image recognition technique with specific energy domain knowledge, the proposed RCNN enables both high recognition accuracy and strong generalization ability. The recognition accuracy of RCNN reached 95.06% for the trained objects and 93.67% for non-trained objects while that of only 75.86% for the conventional CNN. By adopting the presented system optimization method assisted by RCNN, the defrosting frequency, accumulated time and energy consumption were 53.8%, 57.02% and 34.5% less than the original control method. Furthermore, the environmental and cost analysis illustrated that the annual reduction in CO 2 emissions is 2145.21 to 3412.84 kg and the payback time was less than 2.5 years which was far below the service life. The technical feasibility and significant energy-saving benefits of deep learning-based image recognition method were demonstrated through the field experiment. Our study shows the great application potential of image recognition technology and promotes carbon neutrality in industrial energy systems.