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A generative hidden Markov model of the clear-sky index
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Mechanics, Byggteknik.ORCID iD: 0000-0001-6292-0695
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Mechanics, Byggteknik.ORCID iD: 0000-0003-0051-4098
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Mechanics, Byggteknik.ORCID iD: 0000-0003-4887-9547
2019 (English)In: Journal of Renewable and Sustainable Energy, ISSN 1941-7012, E-ISSN 1941-7012, Vol. 11, no 4, article id 043703Article in journal (Refereed) Published
Abstract [en]

Clear-sky index (CSI) generative models are of paramount importance in, e.g., studying the integration of solar power in the electricity grid. Several models have recently been proposed with methodologies that are related to hidden Markov models (HMMs). In this paper, we formally employ HMMs, with Gaussian distributions, to generate CSI time-series. The authors propose two different methodologies. The first is a completely data-driven approach, where an HMM with Gaussian observation distributions is proposed. In the second, the means of these Gaussian observation distributions were predefined based on the fraction of time of bright sunshine from the site. Finally, the authors also propose a novel method to improve the autocorrelation function (ACF) of HMMs in general. The two methods were tested on two datasets representing two different climate regions. The performance of the two methodologies varied between the two datasets and among the compared performance metrics. Moreover, both the proposed methods underperformed in reproducing the ACF as compared to state-of-the-art models. However, the method proposed to improve the ACF was able to reduce the mean absolute error (MAE) of the ACF by up to 19%. In summary, the proposed models were able to achieve a Kolmogorov-Smirnov test score as low as 0.042 and MAE of the ACF as low as 0.012. These results are comparable with the state-of-the-art models. Moreover, the proposed models were fast to train. HMMs are shown to be viable CSI generative models. The code for the model and the simulations performed in this paper can be found in the GitHub repository: HMM-CSI-generativeModel.

Place, publisher, year, edition, pages
2019. Vol. 11, no 4, article id 043703
Keywords [en]
Markov processes, Photovoltaics, Machine learning, Solar energy, Statistical models, Solar irradiance
National Category
Energy Systems Other Environmental Engineering Environmental Sciences
Identifiers
URN: urn:nbn:se:uu:diva-389945DOI: 10.1063/1.5110785ISI: 000482886400012OAI: oai:DiVA.org:uu-389945DiVA, id: diva2:1339916
Funder
StandUpSwedish Energy AgencyAvailable from: 2019-08-01 Created: 2019-08-01 Last updated: 2019-10-02Bibliographically approved

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The full text will be freely available from 2020-08-01 10:37
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Shepero, MahmoudMunkhammar, JoakimWidén, Joakim
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