Hi,
use this example for your purposes:
load census
cdate2 = cdate;
pop2 = pop * 0.5;
curvefit1 = fit(cdate,pop,'poly2');
curvefit2 = fit(cdate2,pop2,'poly2');
X = 1790:1990;
Y1 = curvefit1(X);
Y2 = curvefit2(X);
a = subplot(2,1,1);
plot(cdate,pop, '*', 'color', 'r')
hold on
plot(cdate2,pop2, 'o', 'color', 'b')
b = subplot(2,1,2);
plot(X, Y1, 'color', 'r')
hold on
plot(X, Y2, 'color', 'b')
linkaxes([a b], 'xy')
hold off
this gives you:
.
The code snippet:
curvefit1 = fit (cdate, pop, 'poly2');
curvefit2 = fit (cdate2, pop2, 'poly2');
adjusts the curves for both records individually. If I understand you correctly, that's what you want to do:
Calculate an individual curve adaptation for each individual year and repeat this for all 14 wind turbines. For example, if you look at ten years, you'll get a total of 14 charts, each with 10 individually adjusted curves. So in this case you need 140 times the fit function.
Correct?
What you have to do is hand over the corresponding X, Y pairs for each year to the fit function and repeat this process for a new year with a new curve fit.
To execute this in a for loop you could use this code snippet:
load census
popu = zeros(length(cdata),2);
popu(:,1) = pop;
popu(:,2) = pop * 0.5;
for i = 1:2
Data{i} = fit(cdate,popu(:,i),'poly2');
end
.
Doing so, will give you a cell Array containing for example 140 individual fits, which you can plot as shown above or like you need.
.
Best regards
Stephan
