Heat Supply Prediction Method Based on Improved CNN-BiGRU
An improved CNN-BiGRU heat sup-ply prediction model that integrates Dropout mechanism and Bayesian optimization is proposed.The Dropout mechanism is used to reduce the complexity of the pre-diction model and reduce the risk of the neural network falling into the local optimum.The Bayesian optimiza-tion algorithm is used to optimize the hyper-parameters of the prediction model and improve the prediction ac-curacy of the prediction model.Combined with an ex-ample,the prediction effect of the improved CNN-BiG-RU heat supply prediction model and the influence of the proportion of training set samples on the prediction effect are analyzed.The predicted values of the im-proved CNN-LSTM,CNN-GRU and CNN-BiLSTM pre-diction models significantly deviate from the measured values,while the predicted value of the improved CNN-BiGRU prediction model is closer to the measured val-ue.The improved CNN-BiGRU prediction model is better than other prediction models in all evaluation in-dexes,and the training time is relatively ideal.All things considered,the improved CNN-BiGRU predic-tion model has the best prediction effect.The predic-tion effect improves with the increase of the proportion of training set samples.When the proportion of training set samples reaches 70％,continuingly increasing the proportion of training set samples does not have much help in improving prediction performance.In order to save time and cost,it is recommended to have a train-ing set sample ratio of 70％.
国家科技期刊平台
NSTL
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