Daylight saving time in time series modelling (e.g. load data) I would like to model intraday electricity load data. I have quarter-hourly data for each day of the year. 
In many countries daylight saving time is practive. This has the consequence that once a year a day is 1 hour longer (it has 25 hours) and once a year a day is shorter (only 23 hours). I don't think that changing the time (e.g. to UTC) is a solution here because people go to work at 8:00 a.m. in their "local" time not in UTC.
What I have found in the literature is e.g. here MODELING AND FORECASTING SHORT-TERMELECTRICITY LOAD USING REGRESSION ANALYSIS. There the extra hour is discarded and the missing hours is filled with average values. 
What is the most used procedure here? How do you cope with this probem?
 A: I've been thinking more about my previous answer, and now I'm not so sanguine.
A problem arises because electricity consumption varies by hour depending on both external environmental conditions (especially, temperature), and also on the social conventions that determine work patterns. When daylight savings time begins or ends, the alignment between these two shifts abruptly: the "hour during which the sun sets" may shift from falling during the work day, to falling during evening/dinner-time. 
Hence the challenge involves not just how to edit values immediately at the point of change-over. The question is whether DST and standard time should be considered as, in some sense, distinct regimes.
The care with which you address the issue depends, of course, on what you are going to use the forecast for. For many purposes, it might be OK to just ignore the subtleties, and proceed as per your first proposal. My suggestion remains to try that first, and see if the accuracy of your model is good enough to meet the needs of your specific application.
If results are unsatisfactory, a second stage of complexity might involve breaking your project in half, and creating separate models for the winter regime and the summer regime.  This approach has a lot to recommend it, actually: the relationship between temperature and power consumption is roughly U-shaped, hitting a minimum at about 18 degrees C, reflecting differences in the way temperature changes affect demand for heating versus cooling. Hence whatever model you come up with will end up acting something like the union of two separate regime-specific models anyway. 
A variation on the above -- almost a re-phrasing -- would be to include in your regression equation a DST dummy variable.  That sounds sensible.
Again, the big question is: how much time and effort does it make sense to devote to exploring this issue and it's implications for forecast quality?  If you are doing applied work (as I gather you are), the goal is to craft a model that is fit-to-purpose, rather than devote your life to finding the best of all possible models.  
If you really want to explore this issue, you might look up this paper:

Ryan Kellogg, Hendrik Wolff, Daylight time and energy: Evidence from
  an Australian experiment, Journal of Environmental Economics and
  Management, Volume 56, Issue 3, November 2008, Pages 207-220, ISSN
  0095-0696, 10.1016/j.jeem.2008.02.003.
Keywords: Energy; Daylight saving time;
  Difference-in-difference-in-difference

The authors take advantage of the fact that two Australian states at the same latitude have different rules concerning implementing daylight savings time. This difference creates conditions for a natural experiment regarding the effect of DST on energy consumption, with one state acting as the "treatment group" and its neighbor acting as the "control group".  Additional background is available from Hendrik Wolff's website. It's interesting work -- though perhaps overkill for your application.
A: The suggested procedure -- toss out the extra hour, fill in the missing hour by averaging  nearby values -- strikes me as quite reasonable. The affected hours are in the middle of the night when the system is drawing its base load. There is much less volatility of base loads than of peak loads. Therefore, the forecasting results are unlikely to be sensitive to the details: any reasonable way of accounting for the change-over should yield about the same result in terms of calibrating a forecasting model.
