Maximization Very Short-Term Forecasting of Power Photovoltaic System Using Machine Learning Based on Clearness Index Model

Unit Three Kartini; L. Endah Cahya Ningrum; M. Nur Adiwana

doi:10.12928/biste.v8i3.16181

Authors

Unit Three Kartini Universitas Negeri Surabaya
L. Endah Cahya Ningrum Universitas Negeri Surabaya
M. Nur Adiwana Polytechnic State Madura

DOI:

https://doi.org/10.12928/biste.v8i3.16181

Keywords:

Forecasting, Machine Learning, Clearness Index, Maximization, Photovoltaic

Abstract

The hybrid model for very short-term photovoltaic (PV) power forecasting, covering one hour ahead with 20-minute intervals, combines the k-nearest neighbour (k-NN) and multilayer backpropagation neural network (BP-NN) methods. The uniqueness of this model lies in integrating meteorological and the clarity index. the data preprocessing stage, the k-NN method is applied, while the multilayer BP-NN is used for forecasting. The k-NN Multilayer BP-NN algorithm calculates the nearest data points using Euclidean distance, and then processes the training and testing data through the multilayer BP-NN to generate PV power predictions. The simulation dataset was divided into 70% training data and 30% testing data, with a maximum PV power output of 611 W. The error statistical indicators of machine learning using k-NN-BP-NN model RMSE 27.44 W and MSE 1.5 W. These superior results are attributed to more stable weather patterns and consistent solar radiation. The simulation validity test demonstrated that the k-NN Multilayer BP-NN algorithm achieved better accuracy compared to the k-NN decomposition method. In addition, the model offers high computational efficiency and short inference time, making it highly suitable for real-time PV power forecasting systems.

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