This question is more about an approach to a complicated data situation rather than particular statistical methods.
I'm modeling our organization's electricity bills, and I have monthly billing data from 2008 to the present. I have a fairly accurate model for electricity usage, based on the month's average temperature. And I have a fairly good model for the basics of cost, based on usage, demand, and peak-season surcharges. (The cost model is what I really need, for budgeting purposes.)
BUT the problem is the data. To give an example, in 2010 we received two different sets of credits, based on two different sets of overcharges in 2009, while at the same time we were evidently experiencing the hottest summer and coldest winter in a century, which means that for the next two years (2011-2013) we will be paying a surcharge to make up for that.
It's a messy situation and I'm not sure how to handle it. My first attempt was to modify the data by shifting the refunds to the appropriate period in the past. Unfortunately, one of the refunds is calculated based on data I can't get, so it's just a guess on my part. And who knows how much the two-year surcharge will actually be.
So I'm wondering what the proper approach would be, if any?
- Is it even worth trying to adjust the data? Should I just accept that the bills are the bills and not care about how money is shifting around from year to year? I would think it would result in much higher variance and would mess up trends.
- Could I try to use indicator variables to indicate when various surcharges and refunds hit? I can't believe this would work with only 3-4 years of data.
- Should I try to model the various parts of the bill separately? Some (like the usage and demand charges) might be fairly stable, while others (like refunds or fuel charges) would be appropriately volatile.
- Is it a mistake to try to model monthly bills at all? Should I model at the year level to hopefully smooth out things a bit?
I'd appreciate any ideas or suggestions.