Short time air temperature prediction using pattern approximate matching

Abstract

Accurate prediction of outdoor air temperature is of great significance to improve the energy efficiency of HVAC system and even the whole building. The aim of this paper is to present the development and evaluation of a new algorithm based on pattern approximate matching for the prediction of outdoor air temperature using acquired historical data of five cities in China (Beijing, Harbin, Shanghai, Guangzhou and Kunming) representing different climatic regions. The optimum parameters of the algorithm for each city has been identified by its historical data. The efficiency of the prediction is validated by comparing predicted and measured outdoor air temperature. Furthermore, statistical metrics such as absolute mean error (AME), root mean square error (RMSE), and accuracy were used to evaluate the performance of the algorithm. These metrics confirm the algorithm proposed in this paper is suitable for all the five Chinese cities. All the work has been contacted using Python programming environment.