eprintid: 10200265 rev_number: 8 eprint_status: archive userid: 699 dir: disk0/10/20/02/65 datestamp: 2024-12-13 14:50:57 lastmod: 2024-12-13 14:54:25 status_changed: 2024-12-13 14:50:57 type: proceedings_section metadata_visibility: show sword_depositor: 699 creators_name: Walch, Alina creators_name: Castello, Roberto creators_name: Mohajeri, Nahid creators_name: Guignard, Fabian creators_name: Kanevski, Mikhail creators_name: Scartezzini, Jean-Louis title: Spatio-temporal modelling and uncertainty estimation of hourly global solar irradiance using Extreme Learning Machines ispublished: pub divisions: UCL divisions: B04 divisions: C04 divisions: F34 keywords: Hourly solar irradiance; Extreme Learning Machines; Uncertainty; Satellite data note: 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/. 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. date: 2019-02 date_type: published publisher: Elsevier official_url: http://dx.doi.org/10.1016/j.egypro.2019.01.219 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1907915 doi: 10.1016/j.egypro.2019.01.219 lyricists_name: Mohajeri, Nahid lyricists_id: NMOHA02 actors_name: Mohajeri, Nahid actors_id: NMOHA02 actors_role: owner full_text_status: public pres_type: paper series: Innovative Solutions for Energy Transitions publication: INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS volume: 158 place_of_pub: Amsterdam, The Netherlands pagerange: 6378-6383 pages: 6 event_title: 10th International Conference on Applied Energy (ICAE2018) event_location: Hong Kong, HONG KONG event_dates: 22 Aug 2018 - 25 Aug 2018 issn: 1876-6102 book_title: Energy Procedia editors_name: Yan, Jinyue editors_name: Yang, Hong-xing editors_name: Li, Hailong editors_name: Chen, Xi citation: 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. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10200265/1/1307_PDF_0713113736%20-Energy%20Procedia.pdf