Solar Radiation Prediction using Long Short-Term Memory with Handling of Missing Values and Outliers
Main Article Content
Abstract
The pyranometer sensor is an instrument for measuring Global Horizontal Irradiance (GHI) which is used as parameter for analyzing and predicting weather. GHI data which is processed into prediction model for photovoltaics is useful for determining the performance of solar power generation systems in distributed energy operations. However, GHI sensor data has weaknesses in missing values and outliers due to measurement errors. The research designed a GHI sensor data prediction model using data preprocessing by the imputation of missing values using linear, polynomial, and Piecewise Cubic Hermite Interpolating Polynomials (PCHIP) interpolation and eliminating outliers using Random Sample Consensus (RANSAC) on the dataset. Previous researches show that Long Short-Time Memory (LSTM) can improve the performance of predictions compared to machine learning. This research designs an LSTM prediction model with data preprocessing and without data preprocessing. The results of the imputation of missing values obtained the best performance in PCHIP with Mean Absolute Error (MAE) 39.708 W/m2, Root Mean Absolute Error (RMSE) 76.224 W/m2, Normalized Root Mean Absolute Error (NRMSE) 0.433, and Coefficient Determination (R2) 0.903 then imputation from outlier elimination obtained MAE 44.377 W/m2, RMSE 86.738 W/m2, NRMSE 0.500, and R2 0.886. RANSAC testing succeeded in eliminating 100% outliers. The results of LSTM with data preprocessing obtained better performance with the best evaluations on MAE, RMSE, NRMSE, and R2 for test data of 42.863 W/m2, 82.396 W/m2, 0.396 and 0.918. This study contributes to GHI prediction model that can handle missing values and outliers from sensors to support solar power plants.
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[1]
A. Syahab and M. H. Achamd, “Solar Radiation Prediction using Long Short-Term Memory with Handling of Missing Values and Outliers”, INFOTEL, vol. 16, no. 4, pp. 758-777, Dec. 2024.
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Informatics
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[21] E. Cho, T. W. Chang, and G. Hwang, “Data Preprocessing Combination to Improve the Performance of Quality Classification in the Manufacturing Process,” Electron., vol. 11, no. 3, pp. 1–15, 2022, doi: 10.3390/electronics11030477.
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[29] S. Malakar et al., “Designing a long short-term network for short-term forecasting of global horizontal irradiance,” SN Appl. Sci., vol. 3, no. 4, pp. 1–15, 2021, doi: 10.1007/s42452-021-04421-x.
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[32] D. G. da Silva, M. T. B. Geller, M. S. dos S. Moura, and A. A. de M. Meneses, “Performance evaluation of LSTM neural networks for consumption prediction,” e-Prime - Adv. Electr. Eng. Electron. Energy, vol. 2, no. December 2021, 2022, doi: 10.1016/j.prime.2022.100030.
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[34] H. Liu, Y. Wang, and W. G. Chen, “Three-step imputation of missing values in condition monitoring datasets,” IET Gener. Transm. Distrib., vol. 14, no. 16, pp. 3288–3300, 2020, doi: 10.1049/iet-gtd.2019.1446.
[35] J. He, L. Yuan, H. Lei, K. Wang, Y. Weng, and H. Gao, “Polynomial-Enhanced Convolutional Gated Recurrent Method under Multiple Sensor Feature Fusion for Tool Wear Prediction,” 2024.
[36] R. Solgi, H. A. Loáiciga, and M. Kram, “Long short-term memory neural network (LSTM-NN) for aquifer level time series forecasting using in-situ piezometric observations,” J. Hydrol., vol. 601, 2021, doi: 10.1016/j.jhydrol.2021.126800.
[37] K. Vijayaprabakaran and K. Sathiyamurthy, “Towards activation function search for long short-term model network: A differential evolution based approach,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 6, pp. 2637–2650, 2022, doi: 10.1016/j.jksuci.2020.04.015.
[38] M. Khosravi, B. M. Duti, M. M. S. Yazdan, S. Ghoochani, N. Nazemi, and H. Shabanian, “Multivariate Multi-Step Long Short-Term Memory Neural Network for Simultaneous Stream-Water Variable Prediction,” Eng, vol. 4, no. 3, pp. 1933–1950, 2023, doi: 10.3390/eng4030109.
[39] A. W. Saputra, A. P. Wibawa, U. Pujianto, A. B. Putra Utama, and A. Nafalski, “LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting,” Ilk. J. Ilm., vol. 14, no. 1, pp. 57–62, 2022, doi: 10.33096/ilkom.v14i1.1106.57-62.
[40] C. Liu, A. Zhang, J. Xue, C. Lei, and X. Zeng, “LSTM-Pearson Gas Concentration Prediction Model Feature Selection and Its Applications,” Energies, vol. 16, no. 5, p. 2318, Feb. 2023, doi: 10.3390/en16052318.
[41] H. Fan, M. Jiang, L. Xu, H. Zhu, J. Cheng, and J. Jiang, “Comparison of long short term memory networks and the hydrological model in runoff simulation,” Water (Switzerland), vol. 12, no. 1, pp. 1–15, 2020, doi: 10.3390/w12010175.