Paula Bendiek
Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation
Bendiek, Paula; Taha, Ahmad; Abbasi, Qammer H.; Barakat, Basel
Authors
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%.
Citation
Bendiek, P., Taha, A., Abbasi, Q. H., & Barakat, B. (2022). Solar Irradiance Forecasting Using a Data-Driven Algorithm and Contextual Optimisation. Applied Sciences, 12(1), Article 134. https://doi.org/10.3390/app12010134
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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