دورية أكاديمية
Design of Forecasting Electrical Power of Ultra-Short-Term Solar Power Using the Hybrid Model K-Nearest Neighbors LSTM.
| العنوان: | Design of Forecasting Electrical Power of Ultra-Short-Term Solar Power Using the Hybrid Model K-Nearest Neighbors LSTM. |
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| المؤلفون: | Tri Wahyu Yulianto, Unit Three Kartini, Bambang Suprianto |
| المصدر: | Jurnal Indonesia Sosial Teknologi; jul2024, Vol. 5 Issue 7, p3412-3422, 11p |
| مصطلحات موضوعية: | ELECTRIC power production, K-nearest neighbor classification, SOLAR radiation, SOLAR energy, MACHINE learning |
| مستخلص: | For the application of renewable energy at the airport, the use of solar power requires certainty of the electricity produced. The certainty of electricity generated from solar power can be predicted using machine learning methods. Predictions made on PV electrical power output are based on historical data from direct measurements of solar PV parameters, including solar radiation and PV panel temperature. Various types of machine learning methods for predicting PV output power have been used in previous studies with different evaluation values of prediction results. In this study, the author conducted a hybrid K-NN method with LSTM to predict the PV electrical power of solar PV output with solar radiation parameters and PV panel temperature. After making predictions using this method, excellent RSME results were obtained with a value of 0.015424830635781967. The results of the PV output power value graph in this prediction are also very good, where the predicted value is close to the value of the testing data or actual data. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: | Complementary Index |
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