Vector Representation and Machine Learning for Short-Term Photovoltaic Power Prediction

Short-term photovoltaic (PV) energy production forecasting is critical for managing grid-connected systems and energy trading. Machine learning models are widely used for accurate prediction, and this study proposes using Time2Vec as an embedding for a transformer-based neural network architecture. Experiments on two PV power plants in India showed significant improvements comparing our proposed architecture to MLP, LSTM, and the persis-tence model, which is a standard baseline prediction in this type of forecasting, with over 20 % improvements in some horizons. These findings demonstrate the effectiveness of the proposed approach for short-term PV forecasting using machine learning models.