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

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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), 134. ISSN 2076-3417 [Article]

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Official URL: https://www.mdpi.com/2076-3417/12/1/134

Abstract or Description

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:

Article

Identification Number (DOI):

https://doi.org/10.3390/app12010134

Additional Information:

Funding: This study was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) Grants, EP/T517896/1.

Data Access Statement:

Datasets related to this article can be found at https://nsrdb.nrel.gov/ data-sets/archives.html, hosted by the National Solar Radiation Database (NSRDB) [35], accessed on 20 October 2021.

Keywords:

solar irradiance forecasting; short-term and long-term predictions; machine learning; support vector machine; Facebook Prophet; contextual optimisation

Departments, Centres and Research Units:

Computing

Dates: