I have a task of predicting the electricity load for 2 months of a region based on various parameters, like avg. rainfall, avg. solar radiation, winter or summer. etc.
There are two sets of data given. 1) To analyse and form a model - This contains the parameter and the electricity load values too which should be used to test equation and create a model that will be employed to do the actual work 2) The set of data based on which the prediction is to be made.
I am using neural net fitting to make the model. More clearly the Bayesian Regularization. The train models with this gives me 3.8% Mean avg. percentage error. But the actual forecasting model gives a very high 28% Mean avg. percentage error.
My question is
1) How can I better modify the data to get a better result. I am now thinking to use the Smoothing Spline function to remove the unevenness in the test data and then use the neural net fitting.
2) Is there a better mathematical function that I can use to tackle this problem ?