I am looking some papers and share some experience about the use of grey neural network for irradiance forecast. The main point is the choice of the input and output variable to train the NN.
In my research I want to find a methodology to set up an irradiance forecasting model based on neural networks in which uncertainty is directly taken into account. The approach is based on the use of an artificial neural network whose parameters are represented by grey numbers. The output of the proposed forecasting model is an interval (not a crisp value) which thus directly quantifies the imprecision/uncertainty or the vagueness of the forecasted values. I have long term ambient data (irradiante, temperature, wind speed) to train my neural network.
What is your objective? Is it to determine the irradiance for a given day of the year? As I understand, the data is not continuous for every day of the year. You intend to determine the irradiance for the day you do not have the recorded value and wish to estimate. It is proposed to model through ANN and for validation. In this process, certain uncertainty is bound to exist. Is my understanding correct? Let me know that first