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Integrating Meteorological and PV Data for Short-Term Solar Irradiance Forecasting Using BPNN
Corresponding Author(s) : Ahmad Rizal Agustian
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 1, February 2026
Abstract
Solar power plants are highly dependent on solar radiation intensity, which fluctuates due to changes in atmospheric conditions. To maintain system stability and efficiency, an accurate short-term solar radiation prediction model is essential. This study developed a model for forecasting global solar radiation one hour ahead using the Backpropagation Neural Network (BPNN) method. The dataset was obtained from a photovoltaic (PV) system at Building A8 of Surabaya State University, recorded over four days (June 14-17, 2025) at two-minute intervals. Five input variables were used: clearness index, solar radiation, air temperature, air humidity, and PV output power, resulting in a total of 3,020 data samples. The model was trained through a trial-and-error process by varying the number of neurons, hidden layers, and epochs to determine the optimal configuration. The forecast capability of the model was assessed through four statistical indicators: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The best performance was achieved with a network architecture of 15 input neurons representing input variables resulting from data transformation using the sliding window method, one hidden with 25 neurons, and a single unit in the output layer trained for 2000 epochs, resulting in R2 = 0.98, MAPE = 5.89%, and MSE = 0.00027. The novelty of this research lies in the integration of meteorological data with actual PV power output as model input, enabling the network to capture more realistic nonlinear temporal relationships. The proposed short-term forecasting model provides a practical approach to predicting solar radiation based on historical data and can support efficient energy management and photovoltaic system performance analysis.
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References
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B. H. Mahdi, K. M. Yousif, and L. MS. Dosky, “Using Artificial Neural Networks to Predict Solar Radiation for Duhok City, Iraq,” in 2020 International Conference on Computer Science and Software Engineering (CSASE), IEEE, Apr. 2020, pp. 1–6. https://doi.org/10.1109/CSASE48920.2020.9142119.
I. M. Galván, J. Huertas-Tato, F. J. Rodríguez-Benítez, C. Arbizu-Barrena, D. Pozo-Vázquez, and R. Aler, “Evolutionary-based prediction interval estimation by blending solar radiation forecasting models using meteorological weather types,” Appl Soft Comput, vol. 109, Sep. 2021, https://doi.org/10.1016/j.asoc.2021.107531.
R. J. Jacques Molu et al., “Advancing short-term solar irradiance forecasting accuracy through a hybrid deep learning approach with Bayesian optimization,” Results in Engineering, vol. 23, p. 102461, Sep. 2024, https://doi.org/10.1016/j.rineng.2024.102461.
Y. Lin, D. Duan, X. Hong, X. Cheng, L. Yang, and S. Cui, “Very-Short-Term Solar Forecasting with Long Short-Term Memory (LSTM) Network,” in 2020 Asia Energy and Electrical Engineering Symposium (AEEES), IEEE, May 2020, pp. 963–967. https://doi.org/10.1109/AEEES48850.2020.9121512.
S. Gupta, A. R. Katta, Y. Baldaniya, and R. Kumar, “Hybrid Random Forest and Particle Swarm Optimization Algorithm for Solar Radiation Prediction,” in 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), IEEE, Oct. 2020, pp. 302–307. https://doi.org/10.1109/ICCCA49541.2020.9250715.
P. P. Simanjuntak and K. P. Wibowo, “Estimasi Intensitas Radiasi Matahari Dengan Menggunakan Jaringan Syaraf Tiruan Backprpagation Di Kota Jayapura,” Jurnal Fisika : Fisika Sains dan Aplikasinya, vol. 8, no. 1, pp. 44–49, Apr. 2023, https://doi.org/10.35508/fisa.v8i1.11823.
C.-J. Huang, Y. Ma, and Y.-H. Chen, “Solar Radiation Forecasting based on Neural Network in Guangzhou,” in 2020 International Automatic Control Conference (CACS), IEEE, Nov. 2020, pp. 1–5. https://doi.org/10.1109/CACS50047.2020.9289830.
O. O. Soneye, “Evaluation of clearness index and cloudiness index using measured global solar radiation data: A case study for a tropical climatic region of Nigeria,” Atmosfera, vol. 34, no. 1, pp. 25–39, 2021, https://doi.org/10.20937/ATM.52796 .
O. O. Apeh, O. K. Overen, and E. L. Meyer, “Monthly, seasonal and yearly assessments of global solar radiation, clearness index and diffuse fractions in alice, south africa,” Sustainability (Switzerland), vol. 13, no. 4, pp. 1–15, Feb. 2021, https://doi.org/10.3390/su13042135.
A. E. Radho, P. Sugiartawan, and G. A. Santiago, “Prediksi Jumlah Kasus COVID-19 Menggunakan Teknik Sliding Wondows dengan Metode BPNN,” Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI), vol. 4, no. 1, pp. 12–23, Sep. 2021, https://doi.org/10.22146/jsikti.xxxx.
R. Aprianto, S. Fitriyanto, S. N. Walidain, and Hermansyah, “Artificial Neural Network Backpropagation for Predicting Rainfall (Case Study in Sultan Muhammad Kaharuddin Meteorological Station),” Titian Ilmu: Jurnal Ilmiah Multi Sciences, vol. 15, no. 1, pp. 63–70, Jan. 2023, https://doi.org/10.30599/jti.v15i1.2110.
M. F. Mubarokh, M. Nasir, and D. Komalasari, “Jaringan Syaraf Tiruan Untuk Memprediksi Penjualan Pakaian Menggunakan Algoritma Backpropagation,” Journal of Computer and Information Systems Ampera, vol. 1, no. 1, pp. 2775–2496, Jan. 2020, https://doi.org/10.51519/journalcisa.v1i1.3.
K. Shihab, “A Backpropagation Neural Network for Computer Network Security,” Journal of Computer Science, vol. 2, no. 9, pp. 710–715, 2006.
A. P. Iskandar, “Efektifitas Jaringan Syaraf Tiruan Metode Backpropagation dalam Memprediksi Potensi Banjir ( Studi Kasus : Kecamatan Sungai Serut Bengkulu),” JTIS, vol. 3, no. 2, pp. 79–85, Jul. 2020, https://doi.org/10.36085/jtis.v3i2.159.
D. Kuncoro, A. M. H. Pardede, and S. Syahputra, “Jaringan Syaraf Tiruan Memprediksi Penyakit Gerd menggunakan Metode Backpropagation,” Bridge : Jurnal publikasi Sistem Informasi dan Telekomunikasi, vol. 2, no. 4, pp. 269–287, Sep. 2024, https://doi.org/10.62951/bridge.v2i4.251.
G. I. Diaz, A. Fokoue-Nkoutche, G. Nannicini, and H. Samulowitz, “An effective algorithm for hyperparameter optimization of neural networks,” IBM J Res Dev, vol. 61, no. 4, Jul. 2017, https://doi.org/10.1147/JRD.2017.2709578 .
M. T. Hagan and M. B. Menhaj, “Brief Papers Training Feedforward Networks with the Marquardt Algorithm,” 1994.
I. Pamungkas, Sumadi, and S. Alam, “Studi Komparasi Fungsi Aktivasi Sigmoid Biner, Sigmoid Bipolar dan Linear pada Jaringan Saraf Tiruan dalam Menentukan Warna RGB Menggunakan Matlab,” Serambi Engineering, vol. VII, no. 4, pp. 3749–3758, Oct. 2022, Accessed: Mar. 25, 2025. [Online]. Available: https://ojs.serambimekkah.ac.id/jse/article/download/4776/3596.
S. Sharma, S. Sharma, and A. Athaiya, “ACTIVATION FUNCTIONS IN NEURAL NETWORKS,” International Journal of Engineering Applied Sciences and Technology, vol. 4, no. 12, pp. 310–316, Apr. 2020, https://doi.org/DOI:10.33564/ijeast.2020.v04i12.054.
M. Uzair and N. Jamil, “Effects of Hidden Layers on the Efficiency of Neural networks,” in Proceedings - 2020 23rd IEEE International Multi-Topic Conference, INMIC 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020. https://doi.org/10.1109/INMIC50486.2020.9318195.
T. Deng, “Effect of the Number of Hidden Layer Neurons on the Accuracy of the Back Propagation Neural Network,” Highlights in Science, Engineering and Technology MISBP, vol. 74, 2023, https://doi.org/doi.org/10.54097/nbra6h45.
D. Ardiansyah and D. N. Astuti, “Perbandingan Model Prediksi Radiasi Matahari Berbasis Mesin Pembelajaran Pada Stasiun Meterorologi Fatmawati Soekarno Bengkulu,” Megasains, vol. 14, no. 1, pp. 26–32, Sep. 2023, https://doi.org/10.46824/megasains.v14i1.129.
Moch. N. Adiwana, “Desain Photovoltaic Dan Peramalan Jangka Pendek Radiasi Sinar Matahari Menggunakan Metode Feed-Forward Neural Network,” Jurnal Teknik Elektro, vol. 9, no. 1, pp. 757–764, Jan. 2020.