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Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation

Bendiek, Paula; Taha, Ahmad; Abbasi, Qammer H.; Barakat, Basel

Authors

Paula Bendiek

Ahmad Taha

Qammer H. Abbasi

Basel Barakat



Abstract

Solar forecasting plays a key part in the renewable energy transition. Major challenges, related to load balancing and grid stability, emerge when a high percentage of energy is provided by renewables. These can be tackled by new energy management strategies guided by power forecasts. This paper presents a data-driven and contextual optimisation forecasting (DCF) algorithm for solar irradiance that was comprehensively validated using short- and long-term predictions, in three US cities: Denver, Boston, and Seattle. Moreover, step-by-step implementation guidelines to follow and reproduce the results were proposed. Initially, a comparative study of two machine learning (ML) algorithms, the support vector machine (SVM) and Facebook Prophet (FBP) for solar prediction was conducted. The short-term SVM outperformed the FBP model for the 1- and 2- hour prediction, achieving a coefficient of determination (R2) of 91.2% in Boston. However, FBP displayed sustained performance for increasing the forecast horizon and yielded better results for 3-hour and long-term forecasts. The algorithms were optimised by further contextual model adjustments which resulted in substantially improved performance. Thus, DCF utilised SVM for short-term and FBP for long-term predictions and optimised their performance using contextual information. DCF achieved consistent performance for the three cities and for long- and short-term predictions, with an average R2 of 85%.

Journal Article Type Article
Acceptance Date Dec 7, 2021
Online Publication Date Dec 23, 2021
Publication Date 2022
Deposit Date Mar 10, 2022
Publicly Available Date Mar 10, 2022
Journal Applied Sciences
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 12
Issue 1
Article Number 134
DOI https://doi.org/10.3390/app12010134
Keywords solar irradiance forecasting; short-term and long-term predictions; machine learning; support vector machine; Facebook Prophet; contextual optimisation
Public URL http://researchrepository.napier.ac.uk/Output/2852556

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