Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique

Neethu Elizabeth Michael, Manohar Mishra, Shazia Hasan and Ahmed Al-Durra
Additional contact information
Neethu Elizabeth Michael: Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai P.O. Box 345055, United Arab Emirates
Manohar Mishra: Department of Electrical and Electronics Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan University, Bhubaneswar P.O. Box 751030, India
Shazia Hasan: Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, Dubai Campus, Dubai P.O. Box 345055, United Arab Emirates
Ahmed Al-Durra: Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates

Energies, 2022, vol. 15, issue 6, 1-20

Abstract: Variability in solar irradiance has an impact on the stability of solar systems and the grid’s safety. With the decreasing cost of solar panels and recent advancements in energy conversion technology, precise solar energy forecasting is critical for energy system integration. Despite extensive research, there is still potential for advancement of solar irradiance prediction accuracy, especially global horizontal irradiance. Global Horizontal Irradiance (GHI) (unit: KWh/m 2 ) and the Plane Of Array (POA) irradiance (unit: W/m 2 ) were used as the forecasting objectives in this research, and a hybrid short-term solar irradiance prediction model called modified multi-step Convolutional Neural Network (CNN)-stacked Long-Short-Term-Memory network (LSTM) with drop-out was proposed. The real solar data from Sweihan Photovoltaic Independent Power Project in Abu Dhabi, UAE is preprocessed, and features were extracted using modified CNN layers. The output result from CNN is used to predict the targets using a stacked LSTM network and the efficiency is proved by comparing statistical performance measures in terms of Root Mean Square Error ( RMSE ), Mean Absolute Percentage Error ( MAPE ), Mean Squared Error ( MAE ), and R 2 scores, with other contemporary machine learning and deep-learning-based models. The proposed model offered the best RMSE and R 2 values of 0.36 and 0.98 for solar irradiance prediction and 61.24 with R 2 0.96 for POA prediction, which also showed better performance as compared to the published works in the literature.

Keywords: convolution neural network; deep learning; plane of array (POA) irradiance; solar Irradiance; solar forecasting; stacked LSTM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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