How to deal with time series which change in seasonality or other patterns? Background
I'm working on a time series data set of energy meter readings. The length of the series varies by meter - for some I have several years, others only a few months, etc. Many display significant seasonality, and often multiple layers - within the day, week, or year.
One of the things I've been working on is clustering of these time series. My work is academic for the moment, and while I'm doing other analysis of the data as well, I have a specific goal to carry out some clustering.
I did some initial work where I calculated various features (percentage used on weekends vs. weekday, percentage used in different time blocks, etc.). I then moved on to looking at using Dynamic Time Warping (DTW) to obtain the distance between different series, and clustering based on the difference values, and I've found several papers related to this.
Question
Will the seasonality in a specific series changing cause my clustering to be incorrect? And if so, how do I deal with it?
My concern is that the distances obtained by DTW could be misleading in the cases where the pattern in a time series has changed. This could lead to incorrect clustering.
In case the above is unclear, consider these examples:
Example 1
A meter has low readings from midnight until 8AM, the readings then increase sharply for the next hour and stay high from 9AM until 5PM, then decrease sharply over the next hour and then stay low from 6PM until midnight. The meter continues this pattern consistently every day for several months, but then changes to a pattern where readings simply stay at a consistent level throughout the day.
Example 2
A meter shows approximately the same amount of energy being consumed each month. After several years, it changes to a pattern where energy usage is higher during the summer months before returning to the usual amount.
Possible Directions

I've wondered whether I can continue to compare whole time series, but split them and consider them as a separate series if the pattern changes considerably. However, to do this I'd need to be able to detect such changes. Also, I just don't know if this is a suitable way or working with the data.
I've also considered splitting the data and considering it as many separate time series. For instance, I could consider every day/meter combination as a separate series. However, I'd then need to do similarly if I wanted to consider the weekly/monthly/yearly patterns. I think this would work, but it's potentially quite onerous and I'd hate to go down this path if there's a better way that I'm missing.

Further Notes
These are things that have come up in comments, or things I've thought of due to comments, which might be relevant. I'm putting them here so people don't have to read through everything to get relevant information.

I'm working in Python, but have rpy for those places where R is more suitable. I'm not necessarily looking for a Python answer though - if someone has a practical answer of what should be done I'm happy to figure out implementation details myself.
I have a lot of working &quot;rough draft&quot; code - I've done some DTW runs, I've done a couple of different types of clustering, etc. I think I largely understand the direction I'm taking, and what I'm really looking for is related to how I process my data before finding distances, running clustering, etc. Given this, I suspect the answer would be the same whether the distances between series are calculated via DTW or a simpler Euclidean Distance (ED).
I have found these papers especially informative on time series and DTW and they may be helpful if some background is needed to the topic area: http://www.cs.ucr.edu/~eamonn/selected_publications.htm
After reading your question, I became curious about the topic of time series clustering and dynamic time warping (DTW). So, I have performed a limited search and came up with basic understanding (for me) and the following set of IMHO relevant references (for you). I hope that you'll find this useful, but keep in mind that I have intentionally skipped research papers, as I was more interested in practical aspects of the topic.
Resources:

UCR Time Series Classification/Clustering: main page, software page and corresponding paper
Time Series Classification and Clustering with Python: a blog post and an ipython notebook
Capital Bikeshare: Time Series Clustering: another blog post
Dynamic Time Warping using rpy and Python: another blog post
Mining Time-series with Trillions of Points: Dynamic Time Warping at Scale: another blog post
Time Series Analysis and Mining in R (to add R to the mix): yet another blog post
And, finally, two tools implementing/supporting DTW, to top it off: R package and Python module