Research on energy consumption of a ground source heat pump system based on machine learning algorithms
The HVAC system is the main energy consuming equipment in buildings,and studying the equipment energy consumption model of the air conditioning system plays an important role in formulating efficient optimization strategies.This article compares and predicts the main energy consuming equipment of a ground source heat pump system by obtaining the operating parameters of the system.MATLAB is used for modeling,and backpropagation neural network(BPNN),long short-term memory neural network(LSTM),support vector machine(SVM),random forest(RF),extreme learning machine(ELM),convolutional neural network(CNN),genetic algorithm based inverse neural network(GA-BP)model,and thinking evolution algorithm based inverse neural network(MEA-BP)model are established.The errors of the prediction models are compared.In the prediction of unit energy consumption,the MEA-BP model has the best prediction performance,with root mean square error(RMSE),mean absolute error(MAE),and mean absolute percentage error(MAPE)of 1.487,0.939,and 2.429％,respectively.In the prediction of user pump energy consumption,the GA-BP model has the best prediction effect,with RMSE,MAE,and MAPE of 0.098,0.085,and 2.314％,respectively.In general,the accuracy of the optimized composite model is generally higher than that of the single model,but in some practical projects,individual single models are close to or better than the combined model in specific projects.Therefore,when evaluating the performance of new algorithms,it is necessary to consider them comprehensively and compare them in detail with traditional standalone models.
ground source heat pumpunit energy consumptionuser pump energy consumptionmachine learning algorithmprediction comparison
万方数据
维普
