I have about 1000 samples worth of daily electrical consumption for a building. I'd like to build a predictor based on a number of observable inputs, including:
- daily temperature (continuous)
- hours of sunlight (continuous)
- is_monday, is_tuesday, ... (binary)
- is_holiday_or_weekend (binary)
... and maybe a couple of others. I have some insights into the data. For example, a typical consumption-vs-temperature graph, ignoring all the other inputs has a typical shape dictated by standby power and ramping up due to HVAC as the temperature increases (i.e. isn't a linear function):
I'm admittedly new to advanced statistics & ML, but I'm not afraid to learn what's needed to solve the problem. What I'd like to avoid, though, is going too far down the wrong algorithmic path.
Having read some of the literature, I get the impression that using Support Vector Regression with a Radial Basis Function for the kernel would be a reasonable approach.
But should I be exploring regression techniques or other machine learning techniques?