Energy Consumption Prediction in a Novel Automated Photovoltaic Design Platform

Tiago C Pereira,Samuel Moraes,João Murta-Pina,Fernando Monteiro,Francisco Oliveira,Rui Amaral-Lopes,P Siano

doi:10.1051/e3sconf/202123900014

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.

Highlights

Renewable energies, especially solar, hydro and wind continue increasing worldwide [1]
The first step is to get all the data needed for the optimal system design through a mobile application with a computer-assisted personal interviewing (CAPI) methodology; after that, the data will be sent to the Cloud for optimization of the PV plant; and a report with the optimal project and investment indicators, as the net present value (NPV), the internal rate of return (IRR) and the investment payback time (IPT), is produced
The cases Res2 and NRes2 are solved by the same sub-algorithm. It starts by identifying the big flaws in the provided profile and, for each flaw, a nonlinear autoregressive ANN with external input (NARX) neural network is trained with the previous data and predicts the energy consumption corresponding to that specific failure

Summary

Introduction

Especially solar, hydro and wind continue increasing worldwide [1]. During PV SPREAD application, 15 days of energy consumption (power profiles) and energy bills correspondent to a year are typically collected Considering this acquired data, artificial neural networks (ANN) are a good choice to predict consumption throughout the year, because they have often shown good performances in modelling energy consumption behaviour, which can be increased if the amount of available data is higher than the 15 days. Hashim et al [10], demonstrated that the NARX architecture can achieve promising results if the external variables used to train the networks show good correlations This architecture, as an example, allows the use of a client’s electricity bill as an external variable to improve its energy consumption prediction if both have good correlations. This paper proposes a multi-step algorithm that forecasts energy consumption in specific customer cases that can occur during the design of self-consumption PV power systems, using mainly NARX.

The PV SPREAD Project

Proposed Algorithm

Case Studies

NRes2 Case

Res1 Case

Conclusion