TY  - JOUR
T1  - Very Short-Term Solar Power Forecasting Using Hybrid LSTM-SVM
AU  - Tanyıldızı Ağır, Tuba
PY  - 2025
DA  - June
Y2  - 2025
DO  - 10.17798/bitlisfen.1581731
JF  - Bitlis Eren Üniversitesi Fen Bilimleri Dergisi
PB  - Bitlis Eren University
WT  - DergiPark
SN  - 2147-3129
SP  - 677
EP  - 696
VL  - 14
IS  - 2
LA  - en
AB  - Solar energy is one of the most preferred energy sources among renewable energy sources. Very short-term power forecasting has an important role in the voltage and frequency control of solar energy. However, it provides stability to energy by correcting energy fluctuations in the energy market. In this study, long short term memory (LSTM), support vector machines (SVM) and hybrid LSTM-SVM model were used to estimate PV power in the very short term. The inputs of the models were 60-minute pressure, humidity, temperature, cloudiness and wind speed of Şanlıurfa province in 2022.At the output of the models, the 60-minute power value of the PV panel was obtained. The performances of hybrid LSTM-SVM, LSTM and SVM were compared using mean square error (MSE), root mean square error (RMSE), normalized root mean square error (NRMSE), mean absolute error (MAE) and correlation coefficient (R). In the very short term, PV panel power Hybrid LSTM-SVM, SVM, and LSTM predicted 0.9649, 0.8836 and 0.7255, respectively. The proposed hybrid LSTM-SVM model outperformed the classical LSTM and SVM. The performance metrics of the hybrid LSTM-SVM model, MSE, RMSE, NRMSE, MAE and R, were 9.0098e-04, 0.0300, 0.0318, 0.011 and 0.9823, respectively. The hybrid LSTM-SVM model had high stability and accuracy in very short-term solar power forecasting. Hybrid LSTM-SVM can be used as an alternative method for very short-term solar power forecasting.
KW  - Photovoltaic panel
KW  - Very-short-term energy forecast
KW  - LSTM
KW  - SVM
KW  - Hybrid LSTM-SVM
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UR  - https://doi.org/10.17798/bitlisfen.1581731
L1  - https://dergipark.org.tr/en/download/article-file/4350372
ER  -