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.

Growing oil demand and the gradual depletion of conventional oil reserves by primary extraction has highlighted the need for enhanced oil recovery techniques to increase the potential of existing reservoirs and facilitate the recovery of more complex unconventional oils. This paper describes the interfacial and colloidal forces governing oil film displacement from solid surfaces. Direct contact of oil with the reservoir rock transforms the solid surface from a water-wet to neutrally-wet and oil-wet as a result of the deposition of polar components of the crude oil, with lower oil recovery from oil-wet reservoirs. To enhance oil recovery, chemicals can be added to the injection water to modify the oil-water interfacial tension and solid-oil-water three-phase contact angle. In the presence of certain surfactants and nanoparticles, a ruptured oil film will dewet to a new equilibrium contact angle, reducing the work of adhesion to detach an oil droplet from the solid surface. Dynamics of contact-line displacement are considered and the effect of surface active agents on enhancing oil displacement discussed. The paper is intended to provide an overview of the interfacial and colloidal forces controlling the process of oil film displacement and droplet detachment for enhanced oil recovery. A comprehensive summary of chemicals tested is provided.