SwePub - search: WFRF:(Aupke Phil)

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1.	Aupke, Phil, et al. (author) Impact of Clustering Methods on Machine Learning-based Solar Power Prediction Models 2022 In: 2022 IEEE International Smart Cities Conference (ISC2). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665485616 Conference paper (peer-reviewed)abstract 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.

Aupke, Phil, et al. (author)
Impact of Clustering Methods on Machine Learning-based Solar Power Prediction Models
2022
In: 2022 IEEE International Smart Cities Conference (ISC2). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665485616
Conference paper (peer-reviewed)abstract
- 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.

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