Deep Learning and Self-Powered Sensors Enabled Edge Computing Platform for Predicting Microclimate at Urban Blocks

Abstract

With the development of urban construction and the continuous innovation of science and technology, the progress of smart cities is imperative. Urban local climate prediction is very important in the construction of smart city, but the existing climate prediction methods cannot accurately predict the urban local climate. Therefore, this work provides accurate climate reference for people’s travel and outdoor activities, thus improving the accuracy of local climate prediction in the city. Firstly, the current situation of climate prediction is discussed, and the main indexes of climate prediction and the basic algorithm of climate index prediction are determined. Then, the deep Learning algorithm is introduced and applied to the edge computing (EC) platform, and self-powered sensors are integrated to predict the microclimate index of urban blocks. Using self-powered sensors can achieve zero contact work and improve the research efficiency. Finally, experiments are carried out in two sample cities, and the prediction results are compared with the actual detection results to verify the performance of the prediction model. The results show that the error value of the microclimate index predicted by the deep learning model under EC platform is -6~5, and the climate index with the largest error is the air age. The air age error of city A is about ± 5 s, and that of city B is -6 ~ 4 s. Practice has proved that the deep learning technology has achieved ideal results in the microclimate index prediction of urban blocks on the EC platform and is practical. This work provides technical support for the prediction of urban block climate indicators and helps to improve the accuracy of climate prediction.