Zuo, H.; Zheng, W.; Wang, M.; Zhang, X. Prediction of Heat and Cold Loads of Factory Mushroom Houses Based on EWT Decomposition. Sustainability2023, 15, 15270.
Zuo, H.; Zheng, W.; Wang, M.; Zhang, X. Prediction of Heat and Cold Loads of Factory Mushroom Houses Based on EWT Decomposition. Sustainability 2023, 15, 15270.
Zuo, H.; Zheng, W.; Wang, M.; Zhang, X. Prediction of Heat and Cold Loads of Factory Mushroom Houses Based on EWT Decomposition. Sustainability2023, 15, 15270.
Zuo, H.; Zheng, W.; Wang, M.; Zhang, X. Prediction of Heat and Cold Loads of Factory Mushroom Houses Based on EWT Decomposition. Sustainability 2023, 15, 15270.
Abstract
In view of the problem that the hot and cold loads of mushroom houses are affected by multiple factors such as environment, mushrooms, and equipment, which are difficult to accurately model, this paper proposes a short-term load prediction method based on empirical wavelet transform (EWT) and mixed autoregressive integrated moving average (ARIMA) and convolutional bi-directional short- and long-term attention mechanism (CNN-BiLSTM-Attention). The method first uses the Boruta algorithm to filter the input characteristics, and then uses the EWT method to decompose the mushroom house load data into 4 modal components. Then the Lempel-Ziv method is introduced to divide the modal components into two categories of high and low frequencies, and the CNN-BiLSTM-Attention and ARIMA prediction models are constructed separately. Finally, the two types of prediction results are superimposed and reconstructed to obtain the final load prediction value. The test results show that the Boruta algorithm can effectively filter out the characteristics of key influencing factors. Compared with the Spearman and Pearson correlation coefficient methods, the mean absolute error (MAE) of the prediction results is reduced by 14.72% and 3.75%, respectively; compared with the ensemble empirical mode decomposition (EEMD) method, the EWT method can increase the decomposition and reconstruction error by 103 orders of magnitude, which can effectively improve the prediction accuracy of the model; compared with the individual neural network model, the prediction effect of the model proposed in this paper has obvious advantages, and the MAE, root mean square error (RMSE) and mean absolute percentage error (MAPE) of the prediction results are reduced respectively. 31.06%, 26.52% and 39.27%.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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