Implementasi Deep Learning Dalam Prediksi Real-Time Iradian Surya
DOI:
https://doi.org/10.52158/pvdpsr36Keywords:
Solar Irradiance Forecasting, Convolutional Neural Network, Deep Learning, Time Series Prediction, Renewable Energy.Abstract
Accurate prediction of solar irradiance plays a critical role in the planning and operation of renewable energy systems, particularly for photovoltaic integration and energy management. This study investigates the use of a deep learning approach based solely on Convolutional Neural Networks (CNN) to forecast short-term solar irradiance values. The model is trained using normalized multivariate time series data, which include several meteorological parameters as input features. The CNN architecture is designed to extract temporal patterns from the input sequences and predict radiation intensity at the next time step. Experimental results show that the proposed model achieves strong predictive performance, with a Mean Squared Error (MSE) of 0.0006, Root Mean Squared Error (RMSE) of 0.0242, Mean Absolute Error (MAE) of 0.0184, and a coefficient of determination (R²) of 0.9607. These findings demonstrate that CNN, despite its simplicity, is capable of effectively learning complex temporal relationships in solar irradiance data. Furthermore, the loss curves for both training and validation sets indicate stable convergence without signs of overfitting. The results suggest that CNN-based forecasting models can offer a lightweight and accurate solution for real-time solar prediction applications, especially when computational resources are limited.
References
[1] Husnayain, F. (2023). Studi Tekno-Ekonomi Sistem Fotovoltaik On-Grid Pada Bangunan Industri Kontrol Satelit. Electrices : Jurnal Otomasi Kelistrikan Dan Energi Terbarukan. https://doi.org/10.32722/ees.v5i1.5778.
[2] Failaq, M. R. F., & Nusantara, I. A. P. (2024). Irisan Penguasan Negara dan Desentralisasi dalam Prospek Pengaturan Energi Terbarukan di Indonesia. Jurnal Konstitusi. https://doi.org/10.31078/jk2117.
[3] Bala, R., & Singh, R. (2022). Prediction of Incident Solar Radiation Using a Hybrid Kernel Based Extreme Learning Machine. International Journal on Artificial Intelligence Tools. https://doi.org/10.1142/s0218213023500045
[4] Li, L., Huang, S.-W., Ouyang, Z., & Li, N. (2022, May 20). A Deep Learning Framework for Non-stationary Time Series Prediction. 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA). https://doi.org/10.1109/cvidliccea56201.2022.9824863
[5] Tzelepi, M., Symeonidis, C., Nousi, P., Kakaletsis, E., Tosidis, P., Nikolaidis, N., & Tefas, A. (2023). Deep Learning for Energy Time-Series Analysis and Forecasting. arXiv.Org. https://doi.org/10.48550/arXiv.2306.09129
[6] Wibawa, A. P., Putra Utama, A. B., Elmunsyah, H., Pujianto, U., Dwiyanto, F. A., & Hernandez, L. (2022). Time-series analysis with smoothed Convolutional Neural Network. Journal of Big Data. https://doi.org/10.1186/s40537-022-00599-y
[7] Yuzer, E. O., & Bozkurt, A. (2022). Deep learning model for regional solar radiation estimation using satellite images. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2022.102057
[8] Shenton-Taylor, C. (2023). A convolutional neural network algorithm developed for shielded multi-isotope identification. Journal of Instrumentation. https://doi.org/10.1088/1748-0221/18/05/P05043
[9] Rhouma, A., & Said, Y. (2023). Solar Energy Forecasting Based on Complex Valued Auto-encoder and Recurrent Neural Network. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/ijacsa.2023.0140443
[10] Schipper, F., van Sloun, R. J. G., Grassi, A., Overeem, S., & Fonseca, P. (2023). A deep-learning approach to assess respiratory effort with a chest-worn accelerometer during sleep. Biomedical Signal Processing and Control. https://doi.org/10.1016/j.bspc.2023.104726
[11] Henderson, K., McDermott, T., Van Aken, E. M., & Salado, A. (2022). Towards Developing Metrics to Evaluate Digital Engineering. Systems Engineering. https://doi.org/10.1002/sys.21640
[12] Prawiyogi, A. G., & Anwar, A. S. (2023). Perkembangan Internet of Things (IoT) pada Sektor Energi : Sistematik Literatur Review. https://doi.org/10.34306/mentari.v1i2.254
[13] Alizamir, M., Shiri, J., Fakheri Fard, A., Kim, S., Docheshmeh Gorgij, A., Heddam, S., & Singh, V. P. (2023). Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with wavelet transform. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2023.106199
[14] The Main Stages of the Research Process - A Review of the Literature. (2023). International Journal of Research and Review. https://doi.org/10.52403/ijrr.20230779
[15] Sevli, O., & Okatan, E. (2023). Predicting Solar Radiation Based on Meteorological Data Using Machine Learning Techniques: A Case Study of Isparta. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi. https://doi.org/10.29137/umagd.1268055
[16] Gupta, P., & Tomar, A. (2023). Multi-model approach applied to meteorological data for solar radiation forecasting using data-driven approaches. Optik. https://doi.org/10.1016/j.ijleo.2023.170957
[17] CNN Models Acceleration Using Filter Pruning and Sparse Tensor Core. (2022). https://doi.org/10.15803/ijnc.12.2_270
[18] Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series. (2022, July 18). 2022 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn55064.2022.9892605
[19] Liu, Z., Xu, Z., Jin, J., Shen, Z., & Darrell, T. (2023). Dropout Reduces Underfitting. arXiv.Org.https://doi.org/10.48550/arXiv.2303.01500
[20] Dense Prediction with Attentive Feature Aggregation. (2023, January 1). 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/wacv56688.2023.00018
[21] Firmansyah, I., & Hayadi, B. H. (2022). Komparasi Fungsi Aktivasi Relu Dan Tanh Pada Multilayer Perceptron. JIKO (Jurnal Informatika Dan Komputer). https://doi.org/10.26798/jiko.v6i2.600
[22] Reyad, M. M., Sarhan, A., & Arafa, M. (2023). A modified Adam algorithm for deep neural network optimization. Neural Computing and Applications. https://doi.org/10.1007/s00521-023-08568-z
[23] Anggara, D., Suarna, N., & Wijaya, Y. A. (2023). Analisa perbandingan performa optimizer adam, sgd, dan rmsprop pada model h5. Networking Engineering Research Operation. https://doi.org/10.21107/nero.v8i1.19226
[24] Cabot, J., & Gyang Ross, E. (2023). Evaluating prediction model performance. Surgery. https://doi.org/10.1016/j.surg.2023.05.023
[25] Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geoscientific Model Development. https://doi.org/10.5194/gmd-15-5481-2022
[26] Robeson, S. M., & Willmott, C. J. (2023). Decomposition of the mean absolute error (MAE) into systematic and unsystematic components. PLOS ONE. https://doi.org/10.1371/journal.pone.0279774
[27] Benkirane, S., Guezzaz, A., Azrour, M., & Beni-Hssane, A. (2023). A Novel Machine Learning Approach for Solar Radiation Estimation. Sustainability. https://doi.org/10.3390/su151310609
[28] Gao, Y., Miyata, S., & Akashi, Y. (2022). Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention. Applied Energy. https://doi.org/10.1016/j.apenergy.2022.119288
[29] Golan, T., Siegelman, M., Kriegeskorte, N., & Baldassano, C. (2022). Testing the limits of natural language models for predicting human language judgments. arXiv.Org.https://doi.org/10.48550/arXiv.2204.03592
[2] Failaq, M. R. F., & Nusantara, I. A. P. (2024). Irisan Penguasan Negara dan Desentralisasi dalam Prospek Pengaturan Energi Terbarukan di Indonesia. Jurnal Konstitusi. https://doi.org/10.31078/jk2117.
[3] Bala, R., & Singh, R. (2022). Prediction of Incident Solar Radiation Using a Hybrid Kernel Based Extreme Learning Machine. International Journal on Artificial Intelligence Tools. https://doi.org/10.1142/s0218213023500045
[4] Li, L., Huang, S.-W., Ouyang, Z., & Li, N. (2022, May 20). A Deep Learning Framework for Non-stationary Time Series Prediction. 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA). https://doi.org/10.1109/cvidliccea56201.2022.9824863
[5] Tzelepi, M., Symeonidis, C., Nousi, P., Kakaletsis, E., Tosidis, P., Nikolaidis, N., & Tefas, A. (2023). Deep Learning for Energy Time-Series Analysis and Forecasting. arXiv.Org. https://doi.org/10.48550/arXiv.2306.09129
[6] Wibawa, A. P., Putra Utama, A. B., Elmunsyah, H., Pujianto, U., Dwiyanto, F. A., & Hernandez, L. (2022). Time-series analysis with smoothed Convolutional Neural Network. Journal of Big Data. https://doi.org/10.1186/s40537-022-00599-y
[7] Yuzer, E. O., & Bozkurt, A. (2022). Deep learning model for regional solar radiation estimation using satellite images. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2022.102057
[8] Shenton-Taylor, C. (2023). A convolutional neural network algorithm developed for shielded multi-isotope identification. Journal of Instrumentation. https://doi.org/10.1088/1748-0221/18/05/P05043
[9] Rhouma, A., & Said, Y. (2023). Solar Energy Forecasting Based on Complex Valued Auto-encoder and Recurrent Neural Network. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/ijacsa.2023.0140443
[10] Schipper, F., van Sloun, R. J. G., Grassi, A., Overeem, S., & Fonseca, P. (2023). A deep-learning approach to assess respiratory effort with a chest-worn accelerometer during sleep. Biomedical Signal Processing and Control. https://doi.org/10.1016/j.bspc.2023.104726
[11] Henderson, K., McDermott, T., Van Aken, E. M., & Salado, A. (2022). Towards Developing Metrics to Evaluate Digital Engineering. Systems Engineering. https://doi.org/10.1002/sys.21640
[12] Prawiyogi, A. G., & Anwar, A. S. (2023). Perkembangan Internet of Things (IoT) pada Sektor Energi : Sistematik Literatur Review. https://doi.org/10.34306/mentari.v1i2.254
[13] Alizamir, M., Shiri, J., Fakheri Fard, A., Kim, S., Docheshmeh Gorgij, A., Heddam, S., & Singh, V. P. (2023). Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with wavelet transform. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2023.106199
[14] The Main Stages of the Research Process - A Review of the Literature. (2023). International Journal of Research and Review. https://doi.org/10.52403/ijrr.20230779
[15] Sevli, O., & Okatan, E. (2023). Predicting Solar Radiation Based on Meteorological Data Using Machine Learning Techniques: A Case Study of Isparta. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi. https://doi.org/10.29137/umagd.1268055
[16] Gupta, P., & Tomar, A. (2023). Multi-model approach applied to meteorological data for solar radiation forecasting using data-driven approaches. Optik. https://doi.org/10.1016/j.ijleo.2023.170957
[17] CNN Models Acceleration Using Filter Pruning and Sparse Tensor Core. (2022). https://doi.org/10.15803/ijnc.12.2_270
[18] Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series. (2022, July 18). 2022 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn55064.2022.9892605
[19] Liu, Z., Xu, Z., Jin, J., Shen, Z., & Darrell, T. (2023). Dropout Reduces Underfitting. arXiv.Org.https://doi.org/10.48550/arXiv.2303.01500
[20] Dense Prediction with Attentive Feature Aggregation. (2023, January 1). 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/wacv56688.2023.00018
[21] Firmansyah, I., & Hayadi, B. H. (2022). Komparasi Fungsi Aktivasi Relu Dan Tanh Pada Multilayer Perceptron. JIKO (Jurnal Informatika Dan Komputer). https://doi.org/10.26798/jiko.v6i2.600
[22] Reyad, M. M., Sarhan, A., & Arafa, M. (2023). A modified Adam algorithm for deep neural network optimization. Neural Computing and Applications. https://doi.org/10.1007/s00521-023-08568-z
[23] Anggara, D., Suarna, N., & Wijaya, Y. A. (2023). Analisa perbandingan performa optimizer adam, sgd, dan rmsprop pada model h5. Networking Engineering Research Operation. https://doi.org/10.21107/nero.v8i1.19226
[24] Cabot, J., & Gyang Ross, E. (2023). Evaluating prediction model performance. Surgery. https://doi.org/10.1016/j.surg.2023.05.023
[25] Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. Geoscientific Model Development. https://doi.org/10.5194/gmd-15-5481-2022
[26] Robeson, S. M., & Willmott, C. J. (2023). Decomposition of the mean absolute error (MAE) into systematic and unsystematic components. PLOS ONE. https://doi.org/10.1371/journal.pone.0279774
[27] Benkirane, S., Guezzaz, A., Azrour, M., & Beni-Hssane, A. (2023). A Novel Machine Learning Approach for Solar Radiation Estimation. Sustainability. https://doi.org/10.3390/su151310609
[28] Gao, Y., Miyata, S., & Akashi, Y. (2022). Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention. Applied Energy. https://doi.org/10.1016/j.apenergy.2022.119288
[29] Golan, T., Siegelman, M., Kriegeskorte, N., & Baldassano, C. (2022). Testing the limits of natural language models for predicting human language judgments. arXiv.Org.https://doi.org/10.48550/arXiv.2204.03592
Downloads
Published
2025-12-31
Issue
Section
Articles
License
Copyright (c) 2025 Angga Liwijaya, Pola Risma, Yurni Oktarina, Tresna Dewi
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Implementasi Deep Learning Dalam Prediksi Real-Time Iradian Surya. (2025). Journal of Applied Smart Electrical Network and Systems, 6(2), 90-96. https://doi.org/10.52158/pvdpsr36
