Hi everyone, I really need help from someone who knows the subject and knows how to address me correctly. I am creating neural networks for the prediction of the reactive power at the nodes of the transmission network, having as input to the neural network the active power and indication of month, day, hour, day of the week and type of day.
Training the network on the data of 2017 and 2018, obtaining not excellent but still decent results. The problem arises when I go to use the net for the 2019 reactive forecast. The net fires random values and has sudden high peaks. The data I use is quarterly, I have an input file of 70080 lines for training; i am using a non linear input-output network to make the prediction. Can anyone give me directions on how to improve network performance? The autocorrelation and input-error cross correlation errors are very high, I tried to increase the input delay but by doing so I can only use the trainscg which, on the 2017 2018 data gives me good results but then on the 2019 data is completely lost. Is the network overfitting? Please I really need it. I am attaching an example of the script that I am using and the graphs I get for 2017/2018 and 2019.
An infinite thanks to those who will answer me.