Solar photovoltaic (PV) power forecasting has become an important issue with regard to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, solar irradiance and its accurate forecasting are the prerequisite for solar PV power forecasting. However, previous forecasting approaches using manual feature extraction (MFE), traditional modeling and single deep learning (DL) models could not satisfy the performance requirements in partial scenarios with complex fluctuations. Therefore, an improved DL model based on wavelet decomposition (WD), the Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) is proposed for day-ahead solar irradiance forecasting. Given the high dependency of solar irradiance on weather status, the proposed model is individually established under four general weather type (i.e., sunny, cloudy, rainy and heavy rainy). For certain weather types, the raw solar irradiance sequence is decomposed into several subsequences via discrete wavelet transformation. Then each subsequence is fed into the CNN based local feature extractor to automatically learn the abstract feature representation from the raw subsequence data. Since the extracted features of each subsequence are also time series data, they are individually transported to LSTM to construct the subsequence forecasting model. In the end, the final solar irradiance forecasting results under certain weather types are obtained via the wavelet reconstruction of these forecasted subsequences. This case study further verifies the enhanced forecasting accuracy of our proposed method via a comparison with traditional and single DL models.

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Photodynamic therapy (PDT) has been commonly used in treating many diseases, such as cancer and infectious diseases. We investigated the di®erent e®ects of PDT on three main pathogenic bacteria of periodontitis -Prevotella melaninogenica (P.m.), Porphyromonas gingivalis (P.g.) and Aggregatibacter actinomycetemcomitans (A.a.). The portable red light-emitting diode (LED) phototherapy device was used to assess the exogenous PDT e®ects with di®erent light doses and photosensitizer concentrations (Toluidine blue O, TBO). The portable blue LED phototherapy device was used to assess the endogenous PDT e®ects with the use of endogenous photosensitizers (porphyrin) under di®erent light doses. We found out that both exogenous and endogenous PDT were able to restrict the growth of all the three bacteria signi¯cantly. Moreover, the optimal PDT conditions for these bacteria were obtained through this in vitro screening and could guide the clinical PDT on periodontitis.