Using Deep Learning Approaches to Predict Indoor Thermal and Outdoor Rainfall Probability by Embedded Weather Box

The body of elderly is not only low resistance but also poor temperature regulation and sensitivity. Slight weather changes may cause colds, fever and other diseases. Therefore, how to use a simple application to give people a thermal comfortable living space will be an important issue. Raining can not only affect thermal comfort but also cause inconvenience to people, e.g., shopping or hanging the clothes. This paper aims to use the Arduino weather box to collect the weather data from the living space, and then these data can be analyzed via Support Vector Machine (SVM) and Neural Network (NN) to predict thermal comfort and probability of rainfall. Experimental results show that the accuracy of prediction for temperature using BPN can approximate to 77%, and the accuracy of prediction for rainfall using SVM can approximate to 76%.