A new method is proposed for ultra-short-term prediction of photovoltaic (PV) output, based on an LSTM (long short-term memory)-ARMA (autoregressive moving average) combined model driven by ensemble empirical mode decomposition (EEMD) and aiming to reduce the intermittency and uncertainty of PV power generation. Considering the superposition of the overall trend and local fluctuations contained in the PV output data, an EEMD adaptive decomposition criterion based on continuous mean square error is proposed to extract the various scale components of the PV output data in the time–frequency domain; an ARMA (autoregressive moving average) model suitable for short correlation analysis is constructed for the intrinsic mode function components that characterize local fluctuations of PV output. Environmental parameters such as solar radiation, temperature, and humidity are introduced to construct a LSTM prediction model with autocorrelation capability and environmental characteristics for the EEMD residual that characterizes the overall trend of PV output. Finally, the overall trend and the local fluctuation forecast results are fused to realize an ultra-short-term forecast of PV output. The training set and test set were randomly selected from the PV microgrid system of Hangzhou Dianzi University and used for PV output prediction according to different seasons and weather types. The maximum MAPE on sunny, cloudy, and rainy days was 23.43%, 32.34%, and 33.10%, respectively. The minimum MAPE on sunny, cloudy, and rainy days was 5.53%, 6.47%, and 19.19%, respectively. The results show that the prediction performance of this method is better than traditional models. The ultra-short-term forecasting method for PV output proposed in this paper can help us to improve the safety, flexibility, and robustness of PV power systems.

There is an increasing drug resistance of animal-derived pathogens, seriously posing a huge threat to the health of animals and humans. Traditional drug resistance testing methods are expensive, have low efficiency, and are time-consuming, making it difficult to evaluate overall drug resistance. To develop a better approach to detect drug resistance, a small sample of Escherichia coli resistance data from 2003 to 2014 in Chengdu, Sichuan Province was used, and multiple regression interpolation was applied to impute missing data based on the time series. Next, cluster analysis was used to classify anti-E. coli drugs. According to the classification results, a GM(1,1)-BP model was selected to analyze the changes in the drug resistance of E. coli, and a drug resistance prediction system was constructed based on the GM(1,1)-BP Neural Network model. The GM(1,1)-BP Neural Network model showed a good prediction effect using a small sample of drug resistance data, with a determination coefficient R2 of 0.7830 and an RMSE of only 0.0527. This model can be applied for the prediction of drug resistance trends of other animal-derived pathogenic bacteria, and provides the scientific and technical means for the effective assessment of bacterial resistance.

(1) Background: The high use of antibiotics has made the issue of antimicrobial resistance (AMR) increasingly serious, which poses a substantial threat to the health of animals and humans. However, there remains a certain gap in the AMR system and risk assessment models between China and the advanced world level. Therefore, this paper aims to provide advanced means for the monitoring of antibiotic use and AMR data, and take piglets as an example to evaluate the risk and highlight the seriousness of AMR in China. (2) Methods: Based on the principal component analysis method, a drug resistance index model of anti-E. coli drugs was established to evaluate the antibiotic risk status in China. Additionally, based on the second-order Monte Carlo methods, a disease risk assessment model for piglets was established to predict the probability of E. coli disease within 30 days of taking florfenicol. Finally, a browser/server architecture-based visualization database system for animal-derived pathogens was developed. (3) Results: The risk of E. coli in the main area was assessed and Hohhot was the highest risk area in China. Compared with the true disease risk probability of 4.1%, the result of the disease risk assessment model is 7.174%, and the absolute error was 3.074%. Conclusions: Taking E. coli as an example, this paper provides an innovative method for rapid and accurate risk assessment of drug resistance. Additionally, the established system and assessment models have potential value for the monitoring and evaluating AMR, highlight the seriousness of antimicrobial resistance, advocate the prudent use of antibiotics, and ensure the safety of animal-derived foods and human health.

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