Walch, Alina;
Castello, Roberto;
Mohajeri, Nahid;
Guignard, Fabian;
Kanevski, Mikhail;
Scartezzini, Jean-Louis;
(2019)
Spatio-temporal modelling and uncertainty estimation of hourly
global solar irradiance using Extreme Learning Machines.
In: Yan, Jinyue and Yang, Hong-xing and Li, Hailong and Chen, Xi, (eds.)
Energy Procedia.
(pp. pp. 6378-6383).
Elsevier: Amsterdam, The Netherlands.
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Abstract
Solar photovoltaic (PV) is one of the most promising technologies for the transition from fossil fuels to renewable energy production. Accurate spatial and temporal modelling of solar irradiance is a key factor in the evaluation of PV technology potential for harvesting solar energy. We present here a data-driven approach based on an ensemble of Extreme Learning Machines using geographic and topographic features in input to predict the global horizontal irradiance in Switzerland from coarse-resolution satellite measurements. This provides a precise mapping of hourly global solar irradiance for each (250 × 250) m2 pixel of a grid covering the entire country. The uncertainty on predicted values is quantified through a variance-based analysis, able to distinguish between model and data uncertainty. The former amounts to 1%, whereas the latter is close to 15% of the predicted values. The presented methodology is scalable and applicable to any large environmental dataset. Our modelling of solar irradiance at hourly temporal resolution and of its uncertainty will allow for an estimate of hourly PV potential in Switzerland to facilitate a more efficient integration of solar photovoltaics into the built environment.
| Type: | Proceedings paper |
|---|---|
| Title: | Spatio-temporal modelling and uncertainty estimation of hourly global solar irradiance using Extreme Learning Machines |
| Event: | 10th International Conference on Applied Energy (ICAE2018) |
| Location: | Hong Kong, HONG KONG |
| Dates: | 22 Aug 2018 - 25 Aug 2018 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.egypro.2019.01.219 |
| Publisher version: | http://dx.doi.org/10.1016/j.egypro.2019.01.219 |
| Language: | English |
| Additional information: | This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed, https://creativecommons.org/licenses/by-nc-nd/4.0/. |
| Keywords: | Hourly solar irradiance; Extreme Learning Machines; Uncertainty; Satellite data |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10200265 |
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