Anomaly detection on time series I've just started working on an anomaly detection development in Python.

My data sets are a collection of timeseries. More in details, data are coming from some sensors/meters which record and collect data on boilers or other equipments.

As I said before, the data which I have to work with, are timeseries, so a timestamp and the relative value detected by sensor; a value is anomalous when it's bigger or smaller than the others near it; basically a peak.

I need to develop an unsupervised classification model, because I haven't labels for all data.

Another important aspect, is that this data are "season dependent"; in fact a boiler should be has a higher consumptions in winter than summer. Those values must not be considered as anomalies.

Since I've no experince on this topic, I'm here to ask you, what is the best algorithm/approace to solve this problem.

Furthermore, do you know some books or links to suggest?
You can have a look here, where many open-source algorithms specifically for anomaly detection on time-series data (e.g. metrics) are collected, both for online of offline settings. 

Almost all of them are unsupervised approaches that require no labels to detect the anomalies.

They also automatically handle some of the issues you mentioned, like seasonality.