How to handle multiple periods in data when using Triple Exponential Smoothing (Holt-Winters method)?

Let's say I've got the the following time series (duration = 2.5 years) grouped by hour:

date,numberOfPizzaOrders
2017-04-01 00:00:00,12
2017-04-01 01:00:00,5
2017-04-01 02:00:00,2
2017-04-01 03:00:00,4


The data has a seasonal and trend component (it looks sth like that):

The task is to predict the next 24 values (for each hour of the following day) by using Holt-Winters method only.

My questions: I'm going to play with the following parameters:

1. lenOfPeriod (i.e., data has at least 4 periods: day/week/month/year).
2. len(observedData): pass the whole data ($2.5$ years) or the data for the last month only. I read that $len(observedData) >= 2 * lenOfPeriod$.
3. Differentiate my data ($D_i$ = $X_i$ - $X_{i-24}$ to avoid period=1 day = 24 hours).

Do you have any other recommendations?

Thanks!