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.
Highlights
Photovoltaic (PV) solar systems have become very popular due to the fact that they have seen a surge in efficiency and a decrease in price
The results indicated that temperature, humidity, and clearness index had a more significant effect on the forecasting results and a root mean square error (RMSE) of 0.223 kWh/m2 /day and a mean absolute error (MAE)
Since the noise has a negative effect on forecasting accuracy, this process is of vital importance so as to detect and remove the noise in order to obtain more precise forecasting results
Summary
Photovoltaic (PV) solar systems have become very popular due to the fact that they have seen a surge in efficiency and a decrease in price. Conventional sources of energy such as fossil fuels cause environmental problems such as CO2 emission and other environmental issues. This has been subject to international agreements such as the Conference of Parties 21 (COP21) aiming to invest in renewable energy technologies and reduce the emission of greenhouse gases [1]. Connecting the energy produced by PV arrays to the power grid is challenging because of high variation in solar irradiance levels. Solar irradiance data is an essential factor to design a solar energy system [2], and a shortage of irradiance data has led to a downturn in the use of solar energy [3]
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