To enhance photovoltaic power forecasting performance，a novel prediction model is developed by integrating similarity day classification with an Ivy algorithm-optimized convolutional neural network （CNN）-bidirectional gated recurrent unit （BiGRU）-attention mechanism. First，a dynamic time warping （DTW）-based k-medoids method for selecting similar days is proposed. This approach replaces the Euclidean distance in traditional clustering with DTW distance to achieve nonlinear time series clustering of the historical data，which is classified into three categories：sunny，cloudy，and rainy/snowy. Second，a combined forecasting model based on an Ivy Algorithm-optimized Convolutional Neural Network-Bidirectional Gated Recurrent Unit-Attention Mechanism is introduced.The Ivy Algorithm automatically tunes the hyperparameters，while the CNN extracts spatial features，the BiGRU captures temporal dependencies，and the attention mechanism focuses on key information，thereby collectively improving prediction performance. Finally，a case study is conducted using real operational data from a PV plant in Xinjiang. Results demonstrate the proposed model delivers significantly higher accuracy and faster prediction speed for both long-term and short-term forecasting，while also confirming the effectiveness of the enhanced clustering method.