PREDIKSI POTENSI ENERGI SURYA DAN ANGIN MENGGUNAKAN MODEL LONG SHORT-TERM MEMORY (LSTM) BERBASIS DATA METEOROLOGI

Muh Zulfadli A Suyuti; Taufik  Syam; Nurul  Chairunnisa Noor

doi:10.59003/nhj.v5i10.1934

Authors

Muh Zulfadli A Suyuti Institut Teknologi Bacharuddin Jusuf Habibie
Taufik Syam Badan Riset dan Inovasi Nasional
Nurul Chairunnisa Noor Institut Bacharuddin Jusuf Habibie

DOI:

https://doi.org/10.59003/nhj.v5i10.1934

Keywords:

Solar energy, Wind energy, LSTM, Deep learning, Energy forecasting

Abstract

The global transition toward renewable energy requires accurate forecasting systems to support effective planning and operational management of power generation. This study aims to analyze and forecast solar and wind energy potential using a Long Short-Term Memory (LSTM) deep learning model. The dataset consists of secondary meteorological data from July–August 2025 with an initial 5-minute resolution, resampled into hourly data. The analyzed variables include global horizontal irradiance (GHI), air temperature, and wind speed at 10 meters. Separate models were developed for solar and wind energy forecasting. Solar modeling was conducted during daylight conditions (GHI > 50 W/m²), while wind modeling utilized full 24-hour data. The solar model achieved a Mean Absolute Error (MAE) of 28.02 Watts, RMSE of 34.09 Watts, and an R² value of 0.742. Meanwhile, the wind model obtained an MAE of 4.54 W/m², RMSE of 7.07 W/m², and an R² value of 0.649. These results indicate that the LSTM approach provides good predictive performance for solar energy and moderate performance for wind energy in short-term forecasting.

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References

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

Duffie, J. A., & Beckman, W. A. (2013). Solar Engineering of Thermal Processes (4th ed.). Wiley.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

International Energy Agency. (2022). World Energy Outlook 2022. IEA Publications.

Manwell, J. F., McGowan, J. G., & Rogers, A. L. (2010). Wind Energy Explained: Theory, Design and Application (2nd ed.). Wiley.

REN21. (2023). Renewables 2023 Global Status Report. REN21 Secretariat.

Wang, H., Lei, Z., Zhang, X., Zhou, B., & Peng, J. (2019). A review of deep learning for renewable energy forecasting. Energy Conversion and Management, 198, 111799.

Zhang, Y., Wang, J., & Wang, X. (2018). Review on probabilistic forecasting of wind power generation. Renewable and Sustainable Energy Reviews, 32, 255–270.

Solcast. (2025). Solcast Solar Radiation Data Methodology. Solcast Pty Ltd. https://solcast.com