Forecasting and modelling the uncertainty of low voltage network demand and the effect of renewable energy sources

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dc.contributor.authorAlasali, Feras
dc.contributor.authorFoudeh, Husam
dc.contributor.authorAli, Esraa Mousa
dc.contributor.authorNusair, Khaled
dc.contributor.authorHolderbaum, William
dc.date.accessioned2021-05-21T12:59:20Z
dc.date.available2021-05-21T12:59:20Z
dc.date.issued2021-04-12
dc.description.abstractMore and more households are using renewable energy sources, and this will continue as the world moves towards a clean energy future and new patterns in demands for electricity. This creates significant novel challenges for Distribution Network Operators (DNOs) such as volatile net demand behavior and predicting Low Voltage (LV) demand. There is a lack of understanding of modern LV networks’ demand and renewable energy sources behavior. This article starts with an investigation into the unique characteristics of householder demand behavior in Jordan, connected to Photovoltaics (PV) systems. Previous studies have focused mostly on forecasting LV level demand without considering renewable energy sources, disaggregation demand and the weather conditions at the LV level. In this study, we provide detailed LV demand analysis and a variety of forecasting methods in terms of a probabilistic, new optimization learning algorithm called the Golden Ratio Optimization Method (GROM) for an Artificial Neural Network (ANN) model for rolling and point forecasting. Short-term forecasting models have been designed and developed to generate future scenarios for different disaggregation demand levels from households, small cities, net demands and PV system output. The results show that the volatile behavior of LV networks connected to the PV system creates substantial forecasting challenges. The mean absolute percentage error (MAPE) for the ANN-GROM model improved by 41.2% for household demand forecast compared to the traditional ANN modelen_UK
dc.identifier.citationAlasali F, Foudeh H, Ali EM, et al., (2021) Forecasting and modelling the uncertainty of low voltage network demand and the effect of renewable energy sources. Energies, Volume 14, Issue 8, April 2021, Article number 2151en_UK
dc.identifier.issn1996-1073
dc.identifier.urihttps://doi.org/10.3390/en14082151
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/16710
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectJordanen_UK
dc.subjectrolling and point forecasten_UK
dc.subjectANNen_UK
dc.subjectARIMAX (Autoregressive Integrated Moving Average with explanatory variables)en_UK
dc.subjectPV systemen_UK
dc.subjectLV networken_UK
dc.subjectload forecastingen_UK
dc.titleForecasting and modelling the uncertainty of low voltage network demand and the effect of renewable energy sourcesen_UK
dc.typeArticleen_UK

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