I would like to estimate a regression model of the type:
$ load_t = seasonality_t + trend_t + \beta * temperature_t, $
and I have load data and temperature on high frequency (hourly data). My impression is that the temperature does not influence load at the same frequency as I measure load (i.e. a change in temperature for 2 or 3 hours does not imply a change in load immediately). This is how I understand the application of the concept of coherency as in http://eprints.nuim.ie/1968/1/JR_C81dfisf.pdf
I tried to look at average temperature per day and use this in the regression but the results were not satisfying. How can we incorporate temperature into a practical model in the best way? Any hints? Good references?