It may be beneficial to try and measure units of energy consumed in your district over the last 10 years. Do you have any metering info available from HV infeeds to your area ? Or maybe total units consumed by totalising units billed over the period. This will tell you the load growth. If you are studying the increase in MD over the period. I think you already have the info. I find that the combination of low light levels and rain has the psycological effect of "lets put more heating on " and considerably increases our load demand. If the peaks are too big on your network for your current carrying capability of your system, you may have to improve your load profile by offering cheaper units in your trough periods to "flatten" your peaky profile. Modeling all these numbers can be done in Excel as you are only dealing with a few parameters. Hope this helps.....

Thanks for the reply, we do have all the HV feeder load data available for the past 10 years, I was just wondering what other data I should collect which should be of use in correlating the MD's to other effects (like weather). Your suggestion of light levels and rain may prove useful. Are there any other factors/indications other than weather which anyone has looked at previously. e.g. what about things like house prices, oil prices etc.

Get a good program for multiple linear regression analysis.    Excell has one that will give you coeficients of the regression terms but not any statistical data to analyize it.  The last time ( to long ago to be to much help to you )I did it I used a program called SAS or SASS.
Once you get it you can try correlating power usage with all kinds of things - Like; population, school enrollment, car registration, degree days in a year, square footage of retail space in the area,  employment, unemployment, average income, the dow jones average, runoff from the local river etc.  Agood program will let you put the data in very quickly and test it for degree of correlation.
It's interesting to see what works.  You may find things that correlate with power growth but don't have any causual link.  Get a good statistics book or better take a good stat course.

Try to read Modern Power System Planning Book by McGraw Hill