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A Hybrid GA–MLPNN Model for One-Hour-Ahead Forecasting of the Global Horizontal Irradiance in Elizabeth City, North Carolina

Author

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  • Aydin Jadidi

    (Department of Electrical Engineering, Polytechnic School, Federal University of Bahia, 40210-630 Salvador, Brazil)

  • Raimundo Menezes

    (Department of Electrical Engineering, Polytechnic School, Federal University of Bahia, 40210-630 Salvador, Brazil)

  • Nilmar De Souza

    (Department of Electrical Engineering, Polytechnic School, Federal University of Bahia, 40210-630 Salvador, Brazil)

  • Antonio Cezar De Castro Lima

    (Department of Electrical Engineering, Polytechnic School, Federal University of Bahia, 40210-630 Salvador, Brazil)

Abstract

The use of photovoltaics is still considered to be challenging because of certain reliability issues and high dependence on the global horizontal irradiance (GHI). GHI forecasting has a wide application from grid safety to supply–demand balance and economic load dispatching. Given a data set, a multi-layer perceptron neural network (MLPNN) is a strong tool for solving the forecasting problems. Furthermore, noise detection and feature selection in a data set with numerous variables including meteorological parameters and previous values of GHI are of crucial importance to obtain the desired results. This paper employs density-based spatial clustering of applications with noise (DBSCAN) and non-dominated sorting genetic algorithm II (NSGA II) algorithms for noise detection and feature selection, respectively. Tuning the neural network is another important issue that includes choosing the hidden layer size and activation functions between the layers of the network. Previous studies have utilized a combination of different parameters based on trial and error, which seems to be inefficient in terms of accurate selection of the desired features and also tuning of the neural network. In this research, two different methods—namely, particle swarm optimization (PSO) algorithm and genetic algorithm (GA)—are utilized in order to tune the MLPNN, and the results of one-hour-ahead forecasting of the GHI are subsequently compared. The methodology is validated using the hourly data for Elizabeth City located in North Carolina, USA, and the results demonstrated a better performance of GA in comparison with PSO. The GA-tuned MLPNN reported a normalized root mean square error (nRMSE) of 0.0458 and a normalized mean absolute error (nMAE) of 0.0238.

Suggested Citation

  • Aydin Jadidi & Raimundo Menezes & Nilmar De Souza & Antonio Cezar De Castro Lima, 2018. "A Hybrid GA–MLPNN Model for One-Hour-Ahead Forecasting of the Global Horizontal Irradiance in Elizabeth City, North Carolina," Energies, MDPI, vol. 11(10), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2641-:d:173496
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    References listed on IDEAS

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    1. Hejase, Hassan A.N. & Al-Shamisi, Maitha H. & Assi, Ali H., 2014. "Modeling of global horizontal irradiance in the United Arab Emirates with artificial neural networks," Energy, Elsevier, vol. 77(C), pages 542-552.
    2. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.
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    Cited by:

    1. Aydin Jadidi & Raimundo Menezes & Nilmar de Souza & Antonio Cezar de Castro Lima, 2019. "Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model," Energies, MDPI, vol. 12(10), pages 1-14, May.
    2. Mengting Yao & Yun Zhu & Junjie Li & Hua Wei & Penghui He, 2019. "Research on Predicting Line Loss Rate in Low Voltage Distribution Network Based on Gradient Boosting Decision Tree," Energies, MDPI, vol. 12(13), pages 1-14, June.
    3. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction," Renewable Energy, Elsevier, vol. 190(C), pages 408-424.
    4. Bikhtiyar Ameen & Heiko Balzter & Claire Jarvis & James Wheeler, 2019. "Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks," Energies, MDPI, vol. 12(1), pages 1-28, January.

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