###### Abstract  
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. solar irradiance forecasting; wavelet decomposition; convolutional neural network; recurrent neural network; long short term memory 2018 8 1286 10.3390/app8081286 10.3390/app8081286 10. (IEA) reported that the installed solar PV capacity has already reached more than 300 GW by the end of 2016 [2]. The annual market of solar PV power has increased by nearly 50%. The top five countries, led by China, accounted for 85% of additions [3]. The above phenomena verified that solar PV power was the world's leading source of renewables in 2016. However, the high dependence of solar PV power on geographical locations and weather conditions can lead to the dynamic volatility and randomness characteristics of solar PV output power. This unavoidable phenomenon makes PV power forecasting become an important challenge for the power grid in terms of the effective integration of large-scale PV plants, because accurate solar PV power forecasting can provide expected future PV output power, which provides good guidance for the system operator to design a rational dispatching scheme and maintain the balance between supply and demand sides. At the same time, scheduling PV power and other power reasonably may be helpful for effectively addressing the problems, such as system stability and electric power balance [4]. Therefore, accurate solar PV forecasting is essential for the sustainable and stable operation of the whole power system. In the actual PV stations, its final PV output is affected by a variety of meteorological factors, such as solar irradiance [5], moisture, ambient temperature, wind velocity and barometric pressure. There are two categories of the existing PV forecasting approaches: direct forecast and step-wise forecast. Direct forecast creates a map between historical power data and power forecast values [6; 7]. Differently, the step-wise forecast is comprised of two steps. In the first step, each meteorological factor is predicted at the target time. In the next step, these predicted meteorological factors are then utilized to create a map that can reflect the relationship between these meteorological factors and PV power forecast value. In sum, the reliable information of the relevant meteorological factors is the key to PV power forecasting.