Heating load prediction based on attention long short term memory: A case study of Xingtai

Abstract

An accurate heating load prediction algorithm can play an important role in smart district heating systems (SDHS), which is helpful for realizing on-demand heating and fine control. However, most of the traditional heating load prediction algorithms neglect the indoor temperature feedback from the household and cannot form closed-loop control. This paper designs an intelligent sensor based on the Narrow band Internet of Thing (NB-IoT) to collect the indoor temperature of a typical household and proposes an algorithm based on attention long short term memory (ALSTM) to predict the heating load for an integrated "heat exchange station - heat user". The attention mechanism is designed to obtain more accurate nonlinear prediction models between the heating load and influencing factors, such as indoor temperature, outdoor temperature, and historical heat consumption. A performance comparison with other state-of-the-art algorithms shows that the proposed ALSTM algorithm has the best performance, achieving an accuracy of 97.9%. Besides, a Kalman filter is introduced to identify and remove outliers while reducing the random error of the measurement.