- Choosing an appropriate model based on our current understanding of the system, and what reasonable assumptions we can make about it
- Using curve fitting to fit the model to the data we already have to refine the coefficients for the model
- Qualitative and basic quantitative (but not statistical) analysis of the model, and the assumptions made.
The data used will be real income data for a 2.8 kWh solar panel system located in the UK. We have three years worth of data - let's work out how much we can expect to earn over the 25 year period of the original Feed-in Tariff scheme.
The first thing to understand is that all models require assumptions to be made. There has to be logical reason underlying these assumptions, or the model will lack predictive power. Matlab can not do this for you - it can certainly help - but brute force can't replace sensible reasoning.
