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

Aydin Jadidi, Raimundo Menezes, Nilmar De Souza and Antonio Cezar De Castro Lima
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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

Energies, 2018, vol. 11, issue 10, 1-18

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.

Keywords: global horizontal irradiance; density-based spatial clustering of applications with noise; non-dominated sorted genetic algorithm II; genetic algorithm; multi-layer perceptron neural network (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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