3 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number00014
JournalE3S Web of Conferences
Volume239
DOIs
Publication statusPublished - 10 Feb 2021
Event2020 International Conference on Renewable Energy, ICREN 2020 - Virtual, Online, Italy
Duration: 25 Nov 202027 Nov 2020

Fingerprint

Dive into the research topics of 'Energy Consumption Prediction in a Novel Automated Photovoltaic Design Platform'. Together they form a unique fingerprint.

Cite this