Enhancing Short-Term Solar Photovoltaic Power Forecasting Using a Hybrid Deep Learning Approach

Solar photovoltaic (PV) power generation is gradually increasing, but its intermittent nature poses challenges to grid stability. To address this, advanced forecasting methods, such as deep learning (DL) algorithms, can be employed to ensure a more stable and reliable energy supply. Accurate short-term forecasts are essential for electricity grids to effectively mitigate the impact of solar intermittency and enhance grid performance. This research contributes by developing a hybrid DL model that combines a 1-dimensional convolutional neural network (1D CNN) with a gated recurrent unit (GRU), referred to as "1D CNN-GRU". The 1D CNN module extracts essential features from time series data, such as solar PV power generation, while the GRU component provides high-precision short-term forecasts. Additionally, data preparation techniques, including feature selection using SHapley Additive exPlanations (SHAP), data smoothing with an exponential moving average (EMA), and data augmentation with Gaussian noise, are employed to enhance the performance of the proposed 1D CNN-GRU model. To evaluate the effectiveness of the proposed model, it was compared with other DL models, including CNN, GRU, long short-term memory (LSTM), and CNN-GRU. The forecasting was performed using the Hydro-Floating Solar Plant dataset, obtained from the 45 MW hydro-floating solar installation located at Sirindhorn Dam in Ubon Ratchathani province, Thailand. The proposed 1D CNN-GRU model was tested using data from three different seasons: winter, summer, and the rainy season. The model achieved the lowest root mean square error (RMSE) across all seasons, with values of 0.025 (winter), 0.050 (summer), and 0.094 (rainy), and demonstrated the shortest training time. The forecasting results indicated that the proposed model outperformed all other models in terms of both accuracy and training time.