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Gaussian Process Regression for Probabilistic Short-term Solar Output Forecast

Najibi, F. ORCID: 0000-0002-2866-623X, Apostolopoulou, D. ORCID: 0000-0002-9012-9910 and Alonso, E. ORCID: 0000-0002-3306-695X (2021). Gaussian Process Regression for Probabilistic Short-term Solar Output Forecast. International Journal of Electrical Power and Energy Systems, 130, 106916.. doi: 10.1016/j.ijepes.2021.106916

Abstract

With increasing concerns of climate change, renewable resources such as photovoltaic (PV) have gained popularity as a means of energy generation. The smooth integration of such resources in power system operations is enabled by accurate forecasting mechanisms that address their inherent intermittency and variability. This paper proposes a novel probabilistic framework to predict short-term PV output taking into account the variability of weather data over different seasons. To this end, we go beyond existing prediction methods, building a pipeline of processes, i.e., feature selection, clustering and Gaussian Process Regression (GPR). We make use of datasets that comprise of power output and meteorological data such as irradiance, temperature, zenith, and azimuth. First, a correlation study is performed to select the weather features which affect solar output to a greater extent. Next, we categorise the data into four groups based on solar output and time by using k-means clustering. Finally, we determine a function that relates the aforementioned selected features with solar output by using GPR and Matérn 5/2 as a kernel function. We validate our method with five solar generation plants in different locations and compare the results with existing methodologies. More specifically, in order to test the proposed model, two different methods are used: (i) a 5-fold cross validation; and (ii) holding out 30 random days as test data. To confirm the model accuracy, we apply our framework 30 independent times on each of the four clusters. The average error follows a normal distribution, and with 95% confidence level, it takes values between −1.6% and 1.4%. The proposed framework decreases the normalised root mean square error and mean absolute error by 54.6% and 55.5%, respectively, when compared with other relevant works.

Publication Type: Article
Additional Information: © 2021 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: Short-term forecasting,Photovoltaic, Gaussian Processes Regression, k-means, Feature selection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
School of Mathematics, Computer Science & Engineering > Engineering > Electrical & Electronic Engineering
Date Deposited: 07 Apr 2021 10:20
URI: https://openaccess.city.ac.uk/id/eprint/25876
[img] Text - Accepted Version
This document is not freely accessible until 29 March 2022 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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