Solar irradiance forecasting using a data-driven algorithm and contextual optimisation

Bendiek, P., Taha, A. , Abbasi, Q. H. and Barakat, B. (2022) Solar irradiance forecasting using a data-driven algorithm and contextual optimisation. Applied Sciences, 12(1), 134. (doi: 10.3390/app12010134)

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%.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Taha, Dr Ahmad and Abbasi, Professor Qammer

Creator Roles

Taha, A.: Funding acquisition, Writing – review and editing
Abbasi, Q. H.: Funding acquisition

• Authors: Bendiek, P., Taha, A., Abbasi, Q. H., and Barakat, B.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Applied Sciences
• Publisher: MDPI
• ISSN: 2076-3417
• ISSN (Online): 2076-3417
• Published Online: 23 December 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Applied Sciences 12(1): 134
• Publisher Policy: Reproduced under a Creative Commons License

Related URLs

• Research Data

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
312561		EPSRC DTP 2020/21	Christopher Pearce	Engineering and Physical Sciences Research Council (EPSRC)	EP/T517896/1	Research and Innovation Services