A feature transformation and extraction approach-based artificial neural network for an improved production prediction of grid-connected solar photovoltaic systems

Abstract

ABSTRACT The grid operators face challenges due to the fluctuations in the energy production from Photovoltaic (PV) power plants. Therefore, reliable prediction models are critical as they aid grid operators in optimizing operational planning and lowering operating costs. One of the most challenging components of production prediction accuracy is determining a proper set of features that affect the models’ prediction accuracy. In this work, an accurate data-driven prediction model has been developed based on new timestamps and weather condition features that affect the PV system. Specifically, standard PV physics-based models have been employed for feature transformation and extraction from the traditional available historical features. The new features are then utilized to build an Artificial Neural Network (ANN), whose production predictions are compared to those obtained using the old features and other features extracted using the principal component analysis (PCA). The suggested approach’s efficacy is demonstrated using a 264 kWp PV system in Jordan. The proposed approach has shown superior with performance gain in accuracy reaches up to 21% and 64% compared to the models that employ old and PCA features, respectively. Additionally, the proposed approach reduces the computational cost by 20% and 7% compared to old and PCA models, respectively.