Research On Short And Medium Term Heat Load Forecasting For Scale Heat Settlement Site In Urban Heating System

Heating is the important basic industries and public utility to the national economy and people's life.With the commercialization of heating,heating load forecasting plays a vital role in the safety and economic operation of heating system,and undertakes the basis work of heating system planning,production,dispatch and transaction.The accuracy of heat load forecasting directly affects the quality,safety and economy of heating system.With the heating system continuous improvement of energy-saving emission reduction and intelligent heating demand,the heat load forecasting has become a hot and leading research topic,its study is of great significance on energy-saving emission reduction and management of haze.Because of the difference in big delay,pipeline heat loss,and complexity degree of user categories,the heat load of different heat settlement site have different characteristics in urban heating system.This paper divides the heat load forecasting by the heat settlement site,and analyzes their influence factors.Based on measured data of urban heating system,it studies the theory and method of different scale level heat load forecasting in depth through a variety of intelligent algorithms and the combination of theoretical prediction techniques,and provides a more scientific basis for decision-making of the operation and management of heating system.The main research works and innovations are as follows:(1)Analyze the characteristics of different scale of heat load,the factors affecting the thermal load,and the factors that cause the heat load forecasting error.Through vertically and horizontally pretreated the historical data samples,so that to be more consistent with the actual running trend of the heat load,and then lay the foundation for the short and midum term heat load forecasting by the scale heat settlement site.The correlation analysis is applied to the selection of the input dimension of the heat load forecasting model of each heat settlement site.By this method,the input variable parameters are more correlated with the predicted heat load and make preparations for enhancing the accuracy of the model.Moreover,the parameters of the model are normalized,so as to avoid the decline of the prediction performance.(2)Based on structural risk minimization,Support Vector Machine model optimized by Particle Swarm Optimization algorithm(PSO-SVM model)was proposed which is to forecast heat load of the heat source.The method has a better effect for solving the nonlinear problem of the thermal load of heat source with the outdoor temperature caused by the system with large thermal inertia and large time delay.The paper established three heat load forecasting model,namely,Genetic algorithm(GA)optimizing support vector machine(SVM),Standard support vector machine(SVM)and PSO optimizing support vector machine(SVM),determined the dimension of the input variables of three models by correlation analysis,and verified that the PSO-SVM model is superior to the other two prediction models in both accuracy and generalization ability.(3)For the problems of simplex user type and of large data samples,the paper proposed a heat load forecasting method for heat exchange station which is based on Adaboost combined with multiple weak predictors to construct a stronger predictor.The weak predictor uses BP neural network model with large sample handling and strong fault tolerance,which select PSO to optimize network threshold and weight.Apply Adaboost theory to construct a strong forecasting model by combining nine weak predictors of PSO-BP neural networks.According to the correlation analysis of the thermal load of the heat exchange station and its influence factors,these influence factors of the heat load are selected as the input variables of forecasting model.Finally,through the experiment to compare the method of PSO-BP neural network to the method of traditional BP neural network to proved that the prediction model proposed in this paper can effectively improve the prediction accuracy and generalization ability of heat load in heat exchanging station.(4)In view of the fact that the number of building heat load samples is small and the regulation of the users caused by heat metering is uncertain,two kinds of combination forecasting methods are proposed.Taking the better prediction performance of PSO-SVM model and PSO-BP neural network model as a single method in combination forecasting model,and basing on information entropy theory to extract useful information from single prediction model,then combining the extracted useful information produces a combination method of stronger predictive capabilities.Based on the thought of Adaboost algorithm,the PSO optimizing support vector machine model which will deal with small sample problem is used as weak predictor.utilizing Adaboost theory,it structures a strong prediction model of building heat load composed of eight weak predictors.Making the correlative analysis of the building heat load and its influence factors and finding the suitable input variables for the respective forecasting models.Through the example verification that the above two kinds of combination forecasting methods have higher prediction accuracy than the single prediction model,and the combination methods based on information entropy even better,it can be better on heat load prediction of the residential building.