Transformers-Based Encoder Model for Forecasting Hourly Power Output of Transparent Photovoltaic Module Systems

• Published in: Energies (Basel) 2023-01, Vol.16 (3), p.1353
• Main Authors: Sherozbek, Jumaboev, Park, Jaewoo, Akhtar, Mohammad Shaheer, Yang, O-Bong
• Format: Article
• Language: English
• Subjects: Algorithms; Alternative energy sources; Artificial intelligence; Cloud cover; Deep learning; Electric power grids; Energy efficiency; energy forecasting; Energy resources; Forecasting; GRU; Humidity; LSTM; Machine learning; Neural networks; Photovoltaic cells; Photovoltaic power generation; Photovoltaics; Power; Radiation; Rainfall; Renewable resources; Root-mean-square errors; Solar energy; Solar panels; Solar power; Solar radiation; Statistical methods; Systems stability; Time series; transformers; Weather
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16031353

Solar power generation is usually affected by different meteorological factors, such as solar radiation, cloud cover, rainfall, and temperature. This variability has shown a negative impact on the large-scale integration of solar energy into energy supply systems. For successful integration of solar energy into the electrical grid, it is necessary to predict the accurate power generation by solar panels. In this work, solar power generation forecasting for two types of solar system (non-transparent and transparent panels) was configured by the smart artificial intelligence (AI) modelling. For deep learning models, the dataset obtained from the target value of electricity generation in kWh and other features, such as weather conditions, solar radiance, and insolation. In PV power generation values from non-transparent and transparent solar panels were collected from 1 January to 31 December 2021 with an hourly interval. To prove the efficiency of the proposed model, several deep learning approaches RNN models, such as LSTM, GRU, and transformers models, were implemented. Transformers model for forecasting power generation expressed the best model for non-transparent and transparent solar panels with lower error rates for MAE 0.05 and 0.04, and RMSE 0.24 and 0.21, respectively. The proposed model showed efficient performance and proved effective in forecasting time-series data.