Open AccessResearch and Analysis of Nonlinear Model Identification Control Algorithm Based on Improved Neural RBF For Short Term Heat Load Forecasting of Heat Supply Network
Author(s) -
Xuan Wang,
Wanjun Zhang,
Feng Zhao,
Xiaoping Gou,
Jingxuan Zhang,
Jingyi Zhang
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/714/4/042028
Subject(s) - artificial neural network , matlab , term (time) , nonlinear system , identification (biology) , computer science , heat load , control theory (sociology) , algorithm , control (management) , artificial intelligence , physics , quantum mechanics , biology , operating system , botany , thermodynamics
Aiming at the mismatch between heat supply and demand of heating system, a nonlinear model identification control algorithm based on improved neural network for short-term heat load prediction of heat supply network is proposed by using the characteristics that heat load and temperature of heating system will not change dramatically in a short period of time By using MATLAB simulation, short-term heat load rolling prediction is realized. From the experimental results, this algorithm is better than the traditional RBF neural network in the prediction accuracy, and can accurately predict the trend of heat load.