Short Term Prediction of PV Power Output Generation Using Hierarchical Probabilistic Model

Dongkyu Lee, Jae-Weon Jeong and Guebin Choi
Additional contact information
Dongkyu Lee: Department of Architectural Engineering, Hanyang University, 222 Wangsimni-Ro, Seungdong-Gu, Seoul 04763, Korea
Jae-Weon Jeong: Department of Architectural Engineering, Hanyang University, 222 Wangsimni-Ro, Seungdong-Gu, Seoul 04763, Korea
Guebin Choi: Department of Statistics (Institute of Applied Statistics), Jeonbuk National University, Jeonju 54896, Korea

Energies, 2021, vol. 14, issue 10, 1-15

Abstract: Photovoltaics are methods used to generate electricity by using solar cells, which convert natural energy from the sun. This generation makes use of unlimited natural energy. However, this generation is irregular because they depend on weather occurrences. For this reason, there is a need to improve their economic efficiency through accurate predictions and reducing their uncertainty. Most researches were conducted to predict photovoltaic generation with various machine learning and deep learning methods that have complicated structures and over-fitted performances. As improving the performance, this paper explores the probabilistic approach to improve the prediction of the photovoltaic rate of power output per hour. This research conducted a variable correlation analysis with output values and a specific EM algorithm (expectation and maximization) made from 6054 observations. A comparison was made between the performance of the EM algorithm with five different machine learning algorithms. The EM algorithm exhibited the best performance compared to other algorithms with an average of 0.75 accuracies. Notably, there is the benefit of performance, stability, the goodness of fit, lightness, and avoiding overfitting issues using the EM algorithm. According to the results, the EM algorithm improves photovoltaic power output prediction with simple weather forecasting services.

Keywords: photovoltaic power output prediction; expectation and maximization (EM) algorithm; probabilistic method; correlation analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/10/2822/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/10/2822/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:10:p:2822-:d:554621

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().