### The Content and Contribution of the Paper  
According to the literature review work, we have found that the previous forecasting approaches using manual feature extraction (MFE), traditional modeling and single DL models could not satisfy the performance requirements in partial solar irradiance forecasting scenarios with complex fluctuations. In this paper, we proposed an improved DL model to achieve the performance improvement of day-ahead solar irradiance forecasting. This proposed model is named the DWT-CNN-LSTM model. It should be noted that the historical daily solar irradiance curve always presents high variability and fluctuation since the solar irradiance is influenced by the non-stationary weather conditions. Therefore, the forecasting accuracy of day-ahead solar irradiance strongly depends on the weather statuses no matter what kinds of forecasting models we choose. Given this fact, the DWT-CNN-LSTM models are independently constructed for four general weather types (i.e., sunny, cloudy, rainy, and heavy rainy days). This is because a single forecasting model cannot well reflect the temporal relationships between historical and future solar irradiance under different weather conditions. In other words, classification modeling could reduce the complexity and difficulty of intro-class data fitting to improve the corresponding forecasting accuracy [1; 28].  
The basic pipeline framework behind data-driven DWT-CNN-LSTM models consists of three major parts: (1) Discrete Wavelet Transformation (DWT) based solar irradiance sequence decomposition, (2) a CNN-based local feature extractor, and (3) an LSTM based sequence forecasting model. In solar irradiance forecasting under 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, which leverages the advantage of CNN to automatically learn the abstract feature representation from the raw subsequence data. Since the extracted features 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. Compared to the existing studies for solar irradiance forecasting, the contributions of this paper can be summarized as follows:  
1. Discrete wavelet transformation is applied in our proposed DWT-CNN-LSTM model to decompose the raw solar irradiance sequence data of certain weather types into several stable parts (i.e., low-frequency signals) and fluctuant parts (i.e., high-frequency signals). These decomposed subsequences have better behaviors (e.g., more stable variances and fewer outliers) in terms of regularity than the raw solar irradiance sequence data. Such wavelet decomposition (WD) is helpful for precision improvement of the solar irradiance forecasting model.
2. The CNN and LSTM are perfectly combined in our proposed DWT-CNN-LSTM model, in which the abstract feature representation from the raw subsequence data is effectively extracted by CNN and then these features are fed into LSTM. CNN is good at automatically extracting abstract features from its input, and LSTM is able to find the long dependencies of the time series input.
3. The validity of the proposed DWT-CNN-LSTM model is verified based on the two measured dataset, namely the dataset of Elizabeth City State University and Desert Rock Station.  
The rest of paper is constructed as follows. Section 2 illustrates the three main parts of the proposed DWT-CNN-LSTM model, including DWT based solar irradiance sequence decomposition, the CNN-based local feature extractor, and the LSTM based sequence forecasting model. In Section 3, the details of the experimental simulation are introduced and the relevant analysis results are discussed. Finally, conclusions are drawn in Section 4.