Recent content by mdhastings

  1. M

    Weighting calculation to convert weather data from 6 stations into one

    Thanks again Stephen, For reasons of confidentially, I would prefer to limit access to the document to only you at this time. Could I email the document to you please. If other posters would like access I would prefer to do so on an individual basis.
  2. M

    Weighting calculation to convert weather data from 6 stations into one

    Hi Stephen, No other message - I put the title in the email. My apologies if that is frustrating. I have hunted down how the original calculations were carried out. I wonder if you might still be interested, since I cannot determine Yet the Objective function - it is an optimisation...
  3. M

    Weighting calculation to convert weather data from 6 stations into one

    message to Stephen Tashi and others Hi Stephen, Could you please provide a response of my "Hello are you out there" I have more info available as I have found the report.
  4. M

    Weighting calculation to convert weather data from 6 stations into one

    Thanks Stephen, The way the program is set up shows some interactions with a weather variable (either temp or dew point). The main focus of the program is on matching the days load shape. For that, most are not weather interactions. When I use the daily term sd1 [your sin(...)], I am...
  5. M

    Weighting calculation to convert weather data from 6 stations into one

    I can help here [the above is not right]... recall that the interaction terms are products - so wt1.\sin(\omega[i] t). dow1. \sin(\lambda[i] t) is an 4 way interaction term. wt1 would be a temp (say 10C), the dow1 dummy term is 1 for Monday (otherwise 0) and the 2 sine terms are sequence from...
  6. M

    Weighting calculation to convert weather data from 6 stations into one

    I am programming in R but this can be a tricky language One more question please: In the modified equation I was talking about in my last post how would I set it up to ensure positive coefficients on the weather variables All been good from you Stephen and I deeply appreciated your...
  7. M

    Weighting calculation to convert weather data from 6 stations into one

    All good Stephen. I made a correction in the next quote replacing f with x L = \sum_{i=1}^{ N_f} C[i] f_i(x_1,x_2,..x_n) This is why when you asked whether this was linear at the start I wanted clarity. These is no documentation available of how he thought yet no doubt this is what...
  8. M

    Weighting calculation to convert weather data from 6 stations into one

    Stephen, last try this pdf from Greene's about the interactions
  9. M

    Weighting calculation to convert weather data from 6 stations into one

    Stephen, fourth try this pdf from Greene's about the interactions
  10. M

    Weighting calculation to convert weather data from 6 stations into one

    Stephen, third try this pdf from Greene's about the interactions
  11. M

    Weighting calculation to convert weather data from 6 stations into one

    Stephen, this pdf from Greene's about the interactions
  12. M

    Weighting calculation to convert weather data from 6 stations into one

    I could scan the relevant page in Greene's book - How do I load it into this forum for you to see? The weighting procedure - 6 stations into 1 - but nobody remembers the details or don't understand it. Sorry this just does not make sense to me. I am not giving up - it must be done, but...
  13. M

    Weighting calculation to convert weather data from 6 stations into one

    Stephen only the data is changed - in this case the weather so it is the one algorithm - from my understanding this is the normal econometric method. No in the sense that the weather stations are not matched to areas of population and unfortunately this again is because of closed market and...
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