Gradient boosting models for photovoltaic power estimation under partial shading conditions
File(s)
Author(s)
Nikolaou, N
Batzelis, E
Brown, G
Type
Conference Paper
Abstract
The energy yield estimation of a photovoltaic (PV) system operating under partially shaded conditions is a challenging task and a very active area of research. In this paper, we attack this problem with the aid of machine learning techniques. Using data simulated by the equivalent circuit of a PV string operating under partial shading, we train and evaluate three different gradient boosted regression tree models to predict the global maximum power point (MPP). Our results show that all three approaches improve upon the state-of-the-art closed-form estimates, in terms of both average and worst-case performance. Moreover, we show that even a small number of training examples is sufficient to achieve improved global MPP estimation. The methods proposed are fast to train and deploy and allow for further improvements in performance should more computational resources be available.
Date Issued
2017-11-17
Date Acceptance
2017-09-22
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10691, pp.13-25
ISBN
9783319716428
ISSN
0302-9743
Publisher
Springer
Start Page
13
End Page
25
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
10691
Copyright Statement
© Springer International Publishing AG 2017. The final publication is available at Springer via https://link.springer.com/chapter/10.1007%2F978-3-319-71643-5_2
Source
5th ECML PKDD Workshop, DARE 2017
Subjects
08 Information And Computing Sciences
Artificial Intelligence & Image Processing
Publication Status
Published
Start Date
2017-09-22
Coverage Spatial
Skopje, Macedonia