Prediction of Solar Irradiance Based on Artificial Neural Networks

Abstract

Prediction of solar irradiance plays an essential role in many energy systems. The objective of this paper is to present a low-cost solar irradiance meter based on artificial neural networks (ANN). A photovoltaic (PV) mathematical model of 50 watts and 36 cells was used to extract the short-circuit current and the open-circuit voltage of the PV module. The obtained data was used to train the ANN to predict solar irradiance for horizontal surfaces. The strategy was to measure the open-circuit voltage and the short-circuit current of the PV module and then feed it to the ANN as inputs to get the irradiance. The experimental and simulation results showed that the proposed method could be utilized to achieve the value of solar irradiance with acceptable approximation. As a result, this method presents a low-cost instrument that can be used instead of an expensive pyranometer.

Wavelet Neural Networks for Speed Control of BLDC Motor

“…In wavelet neural networks, both the translation (position) and the dilation are tuning besides weights. The utilization of wavelet node outcomes in efficient networks are optimally approximated and estimated for nonlinear and nonstationary functions [11,12]. There are two main types to construct the wavelet neural network:…”

“…The arrangement of radial basis wavelet neural network (RBWNN) is shown in Figure 1. This network approaches any required signal f(t) by simplifying a linear combination of a group of daughter wavelets ψ a,b , where ψ a,b are created by dilation a and translation b from mother wavelet ψ [11,17].…”

“…The input layer can be represented by a vector x ¼ x 1 ,x 2 , … ,x M ½ , the output layer represented by a vector y ¼ y 1 ,y 2 , … ,y K ÂÃ , and the activation function of hidden layer is the wavelet basis function. The output Y j can be given as follows [11,19]: where, j ¼ 1, 2, 3, … , K; M is the number of inputs; K is the number of output layers; N is the number of hidden layers; g is the bias; and σ u ðÞ is the activation function of the output layer, the most common formula of activation function being sigmoid function which can be illustrated as follows [12]:…”

“…The WNN-PID controller based on PSO is proposed in this section, which combines the ability of the artificial neural networks for learning with the ability of wavelet for identification, control of dynamic system, and also having the capability of self-learning and adapting [10,11,19,37]. Two types of wavelet network are modified in this section, feedforward WNN and proposed recurrent WNN with online tuning optimization using PSO algorithm [22][23][24].…”

“…One hidden layer with four neurons is used. Three types of mother wavelet functions are used and they are: the Mexican hat function is [10,11,22,37,39] ψ…”