An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data

Abdul Rauf Bhatti, Ahmed Bilal Awan, Walied Alharbi, Zainal Salam, Abdullah S. Bin Humayd, Praveen R. P. and Kankar Bhattacharya
Additional contact information
Abdul Rauf Bhatti: Department of Electrical Engineering and Technology, Government College University Faisalabad, Faisalabad 38000, Pakistan
Ahmed Bilal Awan: Department of Electrical and Computer Engineering, College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates
Walied Alharbi: Department of Electrical Engineering, College of Engineering, Majmaah University, Al Majmaah 11952, Saudi Arabia
Zainal Salam: Centre of Electrical Energy Systems, School of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
Abdullah S. Bin Humayd: Department of Electrical Engineering, Umm Al-Qura University, Makkah 21421, Saudi Arabia
Praveen R. P.: Department of Electrical Engineering, College of Engineering, Majmaah University, Al Majmaah 11952, Saudi Arabia
Kankar Bhattacharya: Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada

Sustainability, 2021, vol. 13, issue 21, 1-18

Abstract: In this work, an improved approach to enhance the training performance of an Artificial Neural Network (ANN) for prediction of the output of renewable energy systems is proposed. Using the proposed approach, a significant reduction of the Mean Squared Error (MSE) in training performance is achieved, specifically from 4.45 × 10 −7 to 3.19 × 10 −10 . Moreover, a simplified application of the already trained ANN is introduced through which photovoltaic (PV) output can be predicted without the availability of real-time current weather data. Moreover, unlike the existing prediction models, which ask the user to apply multiple inputs in order to forecast power, the proposed model requires only the set of dates specifying forecasting period as the input for prediction purposes. Moreover, in the presence of the historical weather data this model is able to predict PV power for different time spans rather than only for a fixed period. The prediction accuracy of the proposed model has been validated by comparing the predicted power values with the actual ones under different weather conditions. To calculate actual power, the data were obtained from the National Renewable Energy Laboratory (NREL), USA and from the Universiti Teknologi Malaysia (UTM), Malaysia. It is envisaged that the proposed model can be easily handled by a non-technical user to assess the feasibility of the photovoltaic solar energy system before its installation.

Keywords: solar energy; power system operation; photovoltaics; PV power prediction; Artificial Neural Network (ANN); power forecasting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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