LightGBM and LSTM Cooling Load Prediction Method Based on Time Period Sensitive Weight Combination
Cold load prediction is the foundation of energy-saving control for central air conditioning systems.To further improve the accuracy of cooling load prediction,a weighted combination of LightGBM model and Long Short Term Memory(LSTM)network prediction method were proposed,and different weights were assigned at different time periods.Firstly,preprocess the cooling load data,outdoor temperature,and outdoor humidity,and train them separately according to the input formats of LightGBM model and LSTM network.Secondly,hyperparameter adjustments are made to the evaluation results on the validation set,and the validation set is divided into different time periods.Optimization algorithms are used to obtain the optimal combination weights for each time period.Finally,use actual cooling load data for case analysis.The results indicate that the proposed method can effectively utilize the advantages of the two models at different time periods and has high prediction accuracy.
万方数据
维普
