Issue |
E3S Web Conf.
Volume 239, 2021
International Conference on Renewable Energy (ICREN 2020)
|
|
---|---|---|
Article Number | 00021 | |
Number of page(s) | 13 | |
DOI | https://doi.org/10.1051/e3sconf/202123900021 | |
Published online | 10 February 2021 |
Development of a Computer-Assisted Personal Interviewing (CAPI) Methodology to Assist the Photovoltaic Design Process
1
Engibase – Engenharia e Construção, Lda., 2830-271 Barreiro, Portugal
2
NOVA School of Science and Technology (FCT NOVA), 2829-516 Caparica, Portugal
3
Centre of Technology and Systems (CTS UNINOVA), 2829-516 Caparica, Portugal
4
Digitalmente, Novas Tecnologias de Comunicação, Lda., 3860-384 Estarreja, Portugal
* Corresponding author: jmmp@fct.unl.pt
Computer-Assisted Personal Interviewing (CAPI) is a well-known methodology in the development of social surveys. In this work, CAPI is used to guide the flow of a questionnaire aiming for the acquisition of data and information fundamental to optimise a photovoltaic (PV) design. The questionnaire is implemented in an app, developed in the frame of the PV SPREAD project, which is aimed to support the supplier/designer of PV plants during all the stages of its development. To demonstrate how different choices of a client, specified during the interview with the designer, will have distinct economic results, two configurations are presented. In the first, the system is_allowed to determine and use the optimum inclination angle of the modules, while in the second a low angle is selected by the client, to comply with aesthetic restrictions. The first configuration improves naturally the internal rate of return of the investment, as this is the optimising cost function, but the system allows comparing both ones. The CAPI methodology and its use in the context of PV design show to be a relevant tool to support designers and to provide more informed investments to clients.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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