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Weather-Driven Solar Power Forecasting Using D-Informer: Enhancing Predictions with Climate Variables

Chenglian Ma1, Rui Han1, Zhao An2,*, Tianyu Hu2, Meizhu Jin2

1 School of Electrical Engineering, Northeast Electric Power University, Jilin, 132013, China
2 Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

* Corresponding Author: Zhao An. Email: email

Energy Engineering 2024, 121(5), 1245-1261. https://doi.org/10.32604/ee.2024.046644

Abstract

Precise forecasting of solar power is crucial for the development of sustainable energy systems. Contemporary forecasting approaches often fail to adequately consider the crucial role of weather factors in photovoltaic (PV) power generation and encounter issues such as gradient explosion or disappearance when dealing with extensive time-series data. To overcome these challenges, this research presents a cutting-edge, multi-stage forecasting method called D-Informer. This method skillfully merges the differential transformation algorithm with the Informer model, leveraging a detailed array of meteorological variables and historical PV power generation records. The D-Informer model exhibits remarkable superiority over competing models across multiple performance metrics, achieving on average a 67.64% reduction in mean squared error (MSE), a 49.58% decrease in mean absolute error (MAE), and a 43.43% reduction in root mean square error (RMSE). Moreover, it attained an R² value as high as 0.9917 during the winter season, highlighting its precision and dependability. This significant advancement can be primarily attributed to the incorporation of a multi-head self-attention mechanism, which greatly enhances the model’s ability to identify complex interactions among diverse input variables, and the inclusion of weather variables, enriching the model’s input data and strengthening its predictive accuracy in time series analysis. Additionally, the experimental results confirm the effectiveness of the proposed approach.

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Weather-Driven Solar Power Forecasting Using D-Informer: Enhancing Predictions with Climate Variables

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APA Style
Ma, C., Han, R., An, Z., Hu, T., Jin, M. (2024). Weather-driven solar power forecasting using d-informer: enhancing predictions with climate variables. Energy Engineering, 121(5), 1245-1261. https://doi.org/10.32604/ee.2024.046644
Vancouver Style
Ma C, Han R, An Z, Hu T, Jin M. Weather-driven solar power forecasting using d-informer: enhancing predictions with climate variables. Energ Eng. 2024;121(5):1245-1261 https://doi.org/10.32604/ee.2024.046644
IEEE Style
C. Ma, R. Han, Z. An, T. Hu, and M. Jin "Weather-Driven Solar Power Forecasting Using D-Informer: Enhancing Predictions with Climate Variables," Energ. Eng., vol. 121, no. 5, pp. 1245-1261. 2024. https://doi.org/10.32604/ee.2024.046644



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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