Fig 4 Optimization of hidden unit number
This paper sets the learning rate of the ELMAN neural network to 0.01,
the training times to 1000, the target minimum error to 0.00001, the
momentum factor to 0.01, and the minimum performance gradient to
0.00001.
Given the training duration of the algorithm model, this paper specifies
the number of evolutionary iterations at 100 generations, the population
size at 40, the maximum crossover rate at 0.85, the minimum crossover
rate at 0.55, the minimum mutation rate at 0.35, and the number of
individuals retained through the elitist strategy at 20.
To confirm the effectiveness of the proposed NGA-EMANCNN neural network
prediction model, this study utilizes various statistical error
measurement methods to evaluate the performance of the prediction model,
including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and
Mean Absolute Percentage Error (MAPE).
NGA-LMANNCNN Photovoltaic Output Prediction Model Results: The
paper divides the dataset into training and testing sets at an 8:2
ratio, using the training set to develop the prediction model and the
testing set to assess the model’s performance and practicality
subsequent to training. Given the runtime of the program, only the third
ELMAN sub-neural network is optimized using NGA.
Taking January as an example, the output results of Network 1 on the
test set are shown in Figure 5, the error figure is shown in Figure 6,
and the calculation results of the model measurement index are shown in
Table 1.