Prophet forecast amplitude is too low compared to actual data, daily seasonality is wrong.

I am experiencing issues with having Prophet fit daily usage data. Based on my understanding of the data there is daily, very likely weekly and monthly (possibly annual) components. I have 90d of 15min data. The forecast is interested in next week forecast at 15 min increments. Pretty sure the data is stationary in the data I am using for testing (non stationary is also possible but currently a specific case is used to avoid that).

First when I let prophet do its own thing it does something very odd to the daily seasonality:
`P = Prophet()`
`...`
`P.plot_components(fcast)`
`P.plot(fcast, figsize=(60,6)) #is there a way to plot specific subregion instead of full data?
`

The daily seasonality has way too many bumps in it. The amplitude of daily forecast is also very low. For weekly seasonality there is a large drop around wed, which seems to be exaggerated as the original data (black) has relatively same lows.

I am able to somewhat manage the daily seasonality if I force it myself:
`P = Prophet(daily_seasonality=False)`
`P.add_seasonality(name="daily", period=1, fourier_order=1) #increasing fourier_order to higher number leads to multiple bumps rather than bettermatching of the data `
`...`
`P.plot_components(fcast)`
`P.plot(fcast, figsize=(60,6)) #is there a way to plot specific subregion instead of full data?
`
The data is matched closer but the amplitude of the forecast is still a big issue.

I tried adjusting # of changepoints (ie 90*10) no impact,
changepoint_prior_scale set to 0.17 improves a little bit:

I tried logistic growth not helpful.
Setting daily seasonality to multiplicative goes too ham on the amplitude and the period is offset:
```
P = Prophet(daily_seasonality=False, changepoint_prior_scale=0.17) # daily_seasonality=False, weekly_seasonality=False #, weekly_seasonality=False, changepoint_prior_scale=0.17, n_changepoints=90*10
P.add_seasonality(name="daily", period=1, fourier_order=1, mode="multiplicative")
```
removing changepoint_prior_scale leads to worse match, fourier increase leads to more bumps

Best match with multiplicative and changepoint_prior_scale=0.17 (code above):

Any suggestions as to how I can match the data better (each day is a spike)?