## 4 Conclusions  
The nature of the volatility and randomness characteristics of the output power of solar PV generation causes serious difficulty for the real-time power balance of the interconnected grid. This makes PV power forecasting become an important issue to the power grid in terms of the effective integration of large-scale PV plants. As the main influence factor of PV power generation, the solar irradiance and its accurate forecasting are prerequisites for solar PV power forecasting. Therefore, this paper proposes an improved DL model to enhance the accuracy of day-ahead solar irradiance forecasting. It should be noted that the DWT-CNN-LSTM model is individually established under four general weather types (i.e., sunny, cloudy, rainy and heavy rainy) due to the high dependency of solar irradiance on weather status. The basic pipeline framework behind the data-driven DWT-CNN-LSTM model consists of three major parts: (1) DWT based solar irradiance sequence decomposition; (2) the CNN-based local feature extractor; and (3) the LSTM-based sequence forecasting model. In the 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 to 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. In the case study using two datasets of Elizabeth City State University and Desert Rock Station, the performance of the proposed DWT-CNN-LSTM model is compared with another six solar irradiance forecasting models, namely, CNN-LSTM (i.e., our proposed model without WD), ANN, manually extracted features-ANN, persistence forecasting, CNN, and LSTM. Based on three error indexes (i.e., RMSE, MAE, and R), the simulation results indicate that DWT-CNN-LSTM model has high superiority in the solar irradiance forecasting, especially under extreme weather conditions. This mans the proposed DL technique-based day-ahead solar irradiance forecasting model has high potential for future practical applications. All authors have worked on this manuscript together and all authors have read and approved the final manuscript. [PERSON], [PERSON] and [PERSON]. ([PERSON]) conceived and designed the experiments;  
Figure 15: The visualization of feature maps extracted by CNN from the raw subsequence data. (**a**) the original data before convolution operation; (**b**) The first feature map yielded by convolution operation; (**c**) the second feature map yielded by convolution operation; and (**d**) the third feature map yielded by convolution operation. [PERSON] and [PERSON]. ([PERSON]) performed the experiments; [PERSON], [PERSON], [PERSON] ([PERSON]) analyzed the data; [PERSON] and [PERSON] wrote the paper. **Funding:** This work was supported by the National Key R&D Program of China (2018 YFB0904200), the National Natural Science Foundation of China (51577067), the Beijing Natural Science Foundation of China (3162033), the Hebei Natural Science Foundation of China (E2015502060), the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (LAPS18008), the Science and Technology Project of State Grid Corporation of China (SGCC) (NY7117020), the Open Fund of State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems (China Electric Power Research Institute) (5242001600 FB), and the Fundamental Research Funds for the Central Universities (2018 QN077). **Conflicts of Interest:** The authors declare no conflict of interest.