Linear Predictive Coding and Energy Forecasting - Simple

Hello,

I have a dataset with 274 samples (9 months) of the daily energy (Watts.hour) used on a residential household. I'm not sure if i'm applying the lpc function correctly

My code is the following:

filename='9-months.csv';
energy = csvread(filename);

C=zeros(5,1);
counter=0;

N=3;

for n=274:-1:31

w2=energy(1:n-1,1);
a=lpc(w2,N);

energy_estimated=0; 

      for X = 1:N
      energy_estimated = energy_estimated + (-a(X+1)*energy(n-X));
      end

w_real=energy(n);
error2=abs(w_real-energy_estimated);

counter=counter+1;

C(counter,1)=error2;
end

mean_error=round(mean(C));

Being "n" the sample on analysis, I will use the energy array's values, from 1 to n-1, to calculate the lpc coefficientes (with N=3).

After that, it will apply the calculated coefficients on the "for" cycle presented, in order to calculate the estimated energy.

Finally, error2 outputs the error between the real energy and estimated value.

On the example presented ( http://www.mathworks.com/help/signal/ref/lpc.html ) some filters are used. Do I need to apply any filter to it? Is my methodology correct?

Thank you very much in advance!