摘要：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.
其他摘要：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.
