| Issue |
E3S Web Conf.
Volume 239, 2021
International Conference on Renewable Energy (ICREN 2020)
|
|
|---|---|---|
| Article Number | 00014 | |
| Number of page(s) | 14 | |
| DOI | https://doi.org/10.1051/e3sconf/202123900014 | |
| Published online | 10 February 2021 | |
Energy Consumption Prediction in a Novel Automated Photovoltaic Design Platform
1
NOVA School of Science and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal
2
Center of Technology and Systems (CTS), UNINOVA, 2829-516 Caparica, Portugal
3
Engibase – Engenharia e Construção, Lda., 2830-271 Barreiro, Portugal
4
Digitalmente, Lda., 3860-384 Estarreja, Portugal
* Corresponding author: taa.pereira@campus.fct.unl.pt
This paper describes a multi-step algorithm used to predict and typify the energy consumption profile of a prosumer, allowing the automation of the design of self-consumption photovoltaic (PV) power systems in a novel platform called PV SPREAD. The algorithm uses different methodologies to address various possible scenarios of data availability. In this paper, those scenarios are addressed using nonlinear autoregressive artificial neural networks (ANN) with external inputs (NARX) to predict energy consumption. Results reveal that the proposed algorithm successfully addresses data gaps in a hotel load profile used as a case study. The results also show the limitations of NARX when residential clients are analyzed.
© 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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