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

Bendiek, Paula, Taha, Ahmad, Abbasi, Qammer H. and Barakat, Basel (2021) Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation. Applied Sciences, 12 (1). p. 134. ISSN 2076-3417

Item Type: Article


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%.

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Depositing User: Basel Barakat


Item ID: 14291
Identification Number:
ISSN: 2076-3417
Official URL:

Users with ORCIDS

ORCID for Basel Barakat: ORCID iD

Catalogue record

Date Deposited: 19 Jan 2022 13:34
Last Modified: 25 Jan 2022 08:45


Author: Basel Barakat ORCID iD
Author: Paula Bendiek
Author: Ahmad Taha
Author: Qammer H. Abbasi

University Divisions

Faculty of Technology > School of Computer Science



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