Impact of Clustering Methods on Machine Learning-based Solar Power Prediction Models

Aupke, PhilKarlstads universitet,Institutionen för matematik och datavetenskap (from 2013) (författare)

Prediction of solar power generation is important in order to optimize energy exchanges in future micro-grids that integrate a large amount of photovoltaics. However, an accurate prediction is difficult due to the uncertainty of weather phenomena that impact produced power. In this paper, we evaluate the impact of different clustering methods on the forecast accuracy for predicting hourly ahead solar power when using machine learning based prediction approaches trained on weather and generated power features. In particular, we compare clustering methods using clearness index and K-means clustering, where we use both euclidian distance and dynamic time-warping. For evaluating prediction accuracy, we develop and compare different prediction models for each of the clusters using production data from a swedish SmartGrid. We demonstrate that proper tuning of thresholds for the clearness index improves prediction accuracy by 20.19% but results in worse performance than using K-means with all weather features as input to the clustering.

TEKNIK OCH TEKNOLOGIER Elektroteknik och elektronik Annan elektroteknik och elektronik hsv//swe
ENGINEERING AND TECHNOLOGY Electrical Engineering, Electronic Engineering, Information Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering hsv//eng
Cluster analysis
Electric power transmission networks
K-means clustering
Solar energy
Solar energy conversion
Solar power generation
Weather forecasting
Clearness indices
Clustering methods
Clusterings
Machine-learning
Microgrid
Power
Prediction accuracy
Prediction modelling
Renewable energies
Smart grid
Smart power grids
Computer Science
Datavetenskap

Kassler, Andreas,1968-Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)(Swepub:kau)andrkass (författare)
Theocharis, AndreasKarlstads universitet,Institutionen för ingenjörsvetenskap och fysik (from 2013)(Swepub:kau)andrtheo (författare)
Nilsson, MagnusGlava Energy Center, Arvika (författare)
Andersson, Isac MyrenGlava Energy Center, Arvika (författare)
Karlstads universitetInstitutionen för matematik och datavetenskap (from 2013) (creator_code:org_t)

Ingår i:2022 IEEE International Smart Cities Conference (ISC2): Institute of Electrical and Electronics Engineers (IEEE)9781665485616

Av författaren/redakt...: Aupke, Phil; Kassler, Andreas ...; Theocharis, Andr ...; Nilsson, Magnus; Andersson, Isac ...

Om ämnet

TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Elektroteknik oc ...; och Annan elektrotek ...

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