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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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Abdul Rauf Bhatti & Ahmed Bilal Awan & Walied Alharbi & Zainal Salam & Abdullah S. Bin Humayd & Praveen R. P. & Kankar Bhattacharya, 2021. "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," Sustainability, MDPI, vol. 13(21), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11893-:d:666278
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    References listed on IDEAS

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    Cited by:

    1. Candra Saigustia & Paweł Pijarski, 2023. "Time Series Analysis and Forecasting of Solar Generation in Spain Using eXtreme Gradient Boosting: A Machine Learning Approach," Energies, MDPI, vol. 16(22), pages 1-14, November.
    2. Xilong Lin & Yisen Niu & Zixuan Yan & Lianglin Zou & Ping Tang & Jifeng Song, 2024. "Hybrid Photovoltaic Output Forecasting Model with Temporal Convolutional Network Using Maximal Information Coefficient and White Shark Optimizer," Sustainability, MDPI, vol. 16(14), pages 1-20, July.
    3. Wen-Chang Tsai & Chia-Sheng Tu & Chih-Ming Hong & Whei-Min Lin, 2023. "A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation," Energies, MDPI, vol. 16(14), pages 1-30, July.

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