A Deep Learning Method for Photovoltaic Power Generation Forecasting Based on a Time-Series Dense Encoder

Xingfa Zi (), Feiyi Liu, Mingyang Liu and Yang Wang
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Xingfa Zi: School of Physics, Electrical and Energy Engineering, Chuxiong Normal University, Chuxiong 675000, China
Feiyi Liu: School of Physics, Electrical and Energy Engineering, Chuxiong Normal University, Chuxiong 675000, China
Mingyang Liu: School of Physics, Electrical and Energy Engineering, Chuxiong Normal University, Chuxiong 675000, China
Yang Wang: School of Big Data and Basic Science, Shandong Institute of Petroleum and Chemical Technology, Dongying 257061, China

Energies, 2025, vol. 18, issue 10, 1-22

Abstract: Deep learning has become a widely used approach in photovoltaic (PV) power generation forecasting due to its strong self-learning and parameter optimization capabilities. In this study, we apply a deep learning algorithm, known as the time-series dense encoder (TiDE), which is an MLP-based encoder–decoder model, to forecast PV power generation. TiDE compresses historical time series and covariates into latent representations via residual connections and reconstructs future values through a temporal decoder, capturing both long- and short-term dependencies. We trained the model using data from 2020 to 2022 from Australia’s Desert Knowledge Australia Solar Centre (DKASC), with 2023 data used for testing. Forecast accuracy was evaluated using the R 2 coefficient of determination, mean absolute error (MAE), and root mean square error (RMSE). In the 5 min ahead forecasting test, TiDE demonstrated high short-term accuracy with an R 2 of 0.952, MAE of 0.150, and RMSE of 0.349, though performance declines for longer horizons, such as the 1 h ahead forecast, compared to other algorithms. For one-day-ahead forecasts, it achieved an R 2 of 0.712, an MAE of 0.507, and an RMSE of 0.856, effectively capturing medium-term weather trends but showing limited responsiveness to sudden weather changes. Further analysis indicated improved performance in cloudy and rainy weather, and seasonal analysis reveals higher accuracy in spring and autumn, with reduced accuracy in summer and winter due to extreme conditions. Additionally, we explore the TiDE model’s sensitivity to input environmental variables, algorithmic versatility, and the implications of forecasting errors on PV grid integration. These findings highlight TiDE’s superior forecasting accuracy and robust adaptability across weather conditions, while also revealing its limitations under abrupt changes.

Keywords: photovoltaic energy; renewable energy; PV power generation forecasting; time-series forecasting; deep learning; time-series dense encoder (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: 2025
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