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

Jumaboev Sherozbek, Jaewoo Park, Mohammad Shaheer Akhtar () and O-Bong Yang ()
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Jumaboev Sherozbek: Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, Republic of Korea
Jaewoo Park: Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, Republic of Korea
Mohammad Shaheer Akhtar: Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, Republic of Korea
O-Bong Yang: Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju 54896, Republic of Korea

Energies, 2023, vol. 16, issue 3, 1-11

Abstract: 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.

Keywords: transformers; solar energy; time-series; energy forecasting; LSTM; GRU (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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