Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation

Table 1

Hours of operation for intraday market sessions.

	1st session	2nd session	3rd session	4th session	5th session	6th session
Session opens	17:00	21:00	01:00	04:00	08:00	12:00
Session closes	18:45	21:45	01:45	04:45	08:45	12:45
Matching	19:30	22:30	02:30	05:30	09:30	13:30
Reception of scheduling disaggregations	19:50	22:50	02:50	05:50	09:50	13:50
Release PHF	20:45	23:45	03:45	06:45	10:45	14:45
Scheduling horizon	27 h	24 h	20 h	17 h	13 h	9 h
Time periods	22–24	1–24	5–24	8–24	12–24	16–24