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The accurate forecast of integrated energy loads, which has important practical significance, is the premise of the design, operation, scheduling and management of integrated energy systems. In order to make full use of the coupling characteristics of electricity, cooling and heating loads which is difficult to deal with by traditional methods, this paper proposes a new forecast model of integrated energy system loads based on the combination of convolutional neural network (CNN) and long short term memory (LSTM). Firstly, the Pearson correlation coefficients among the electricity, cooling and heating load series of the integrated energy system are calculated, and the results show that there is a strong coupling relationship between the loads of an integrated energy system. Then, the CNN-LSTM composite model is constructed, and CNN is used to extract the characteristic quantity which reflects the load coupling characteristics of the integrated energy system. Then, the characteristic quantity is converted into the time series input to LSTM, and the excellent time series processing ability of LSTM is used for load forecasting. The results show that the CNN-LSTM composite model proposed in this paper has higher prediction accuracy than the wavelet neural network model, CNN model and LSTM model.

This paper proposes a short-term load forecasting method that takes into account the correlation of integrated energy load. The method use wavelet packet to decompose the electric cooling and heating load in frequency bands, analyze the cross-correlation of the electric cooling and heating load in each frequency band, and choose different forecasting methods according to the strength of the correlation to reflect the cross-correlation of the load itself; the method use recurrent neural network as a forecasting model to reflect the autocorrelation of the load itself. Compared with putting the electric cooling and heating load into the same recurrent neural network or back propagation neural network for forecasting, the method in this paper considers the autocorrelation of the electric cooling and heating load itself and the cross- correlation of the electric cooling and heating load in different frequency bands. This method reduces the average absolute percentage error of the load forecasting.

Background: Expression of glycoprotein A dominant repeat (GARP) has been reported to occur only in activated human naturally occurring regulatory T cells (Tregs) and their clones, and not in activated effector T cells, indicating that GARP is a marker for bona fide Tregs. A different phenotype of chronic obstructive pulmonary disease (COPD) may have a different immunologic mechanism.Objective: To investigate whether the distribution of Tregs defined by GARP is related to the multi-organ loss of tissue phenotype in COPD.Methods: GARP expression on T cells from peripheral blood and bronchoalveolar lavage (BAL) collected from patients with COPD was examined by flow cytometry. The correlation of GARP expression to clinical outcomes and clinical phenotype, including the body mass index, lung function and quantitative computed tomography (CT) scoring of emphysema, was analyzed.Results: Patients with more baseline emphysema had lower forced expiratory volume, body mass index (BMI), worse functional capacity, and more osteoporosis, thus, resembling the multiple organ loss of tissue (MOLT) phenotype. Peripheral Foxp3+GARP+ Tregs are reduced in COPD patients, and this reduction reversely correlates with quartiles of CT emphysema severity in COPD. Meanwhile, the frequencies of Foxp3+GARP- Tregs, which are characteristic of pro-inflammatory cytokine production, are significantly increased in COPD patients, and correlated with increasing quartiles of CT emphysema severity in COPD. Tregs in BAL show a similar pattern of variation in peripheral blood.Conclusion: Decreased GARP expression reflects more advanced disease in MOLT phenotype of COPD. Our results have potential implications for better understanding of the immunological nature of COPD and the pathogenic events leading to lung damage. [ABSTRACT FROM AUTHOR]