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.
|Journal||E3S Web of Conferences|
|Publication status||Published - 10 Feb 2021|
|Event||2020 International Conference on Renewable Energy, ICREN 2020 - Virtual, Online, Italy|
Duration: 25 Nov 2020 → 27 Nov 2020