TY - JOUR
TI - Short term load prediction of regional heating and heat storage system based on neural network
AU - Liu Yang
AU - Liu Huijie
JN - Thermal Science
PY - 2024
VL - 28
IS - 2
SP - 1183
EP - 1190
PT - Article
AB - Accurate heat load prediction is the key to achieve fine control, energy  conservation, and carbon reduction of regional hydronics. Taking the  regional hydronics of a city in the north of China as the research object,  the author, respectively uses back propagation neural network (BPNN),  genetic algorithm (GA) optimized BPNN (GA-BPNN), and autoregressive  integrated moving average model (ARI­MA) combined BPNN (ARIMA BPNN) to  predict its heat load, and compares the accuracy and applicability of each  prediction method. The results indicate that GA-BPNN has the smallest  prediction error, followed by ARIMA-BPNN, but the latter requires less data  for prediction. In practical engineering, if there is a sufficient amount  of data related to heat load, it is recommended to use GA-BPNN. If there is  a small amount of data, ARIMA-BP prediction method can be used.