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Correction published on 16 March 2022, see Energies 2022, 15(6), 2157.
Article

Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources

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Department of Electrical Engineering, The Hashemite University, Zarqa 13113, Jordan
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School of Aerospace, Transport and Manufacturing, Cranfield University, Wharley End, Bedford MK43 0AL, UK
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Department of Aircraft Maintenance, Amman Arab University, Amman 11953, Jordan
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Protection and Metering Department, National Electric Power Company, Amman 11181, Jordan
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Aston Institute of Materials Research, Aston University, Birmingham B47ET, UK
*
Authors to whom correspondence should be addressed.
Academic Editor: Luis Hernández-Callejo
Energies 2021, 14(8), 2151; https://doi.org/10.3390/en14082151
Received: 24 March 2021 / Revised: 8 April 2021 / Accepted: 9 April 2021 / Published: 12 April 2021 / Corrected: 16 March 2022
More 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 model. View Full-Text
Keywords: load forecasting; LV network; PV system; ARIMAX (Autoregressive Integrated Moving Average with explanatory variables); ANN; rolling and point forecast; Jordan load forecasting; LV network; PV system; ARIMAX (Autoregressive Integrated Moving Average with explanatory variables); ANN; rolling and point forecast; Jordan
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MDPI and ACS Style

Alasali, F.; Foudeh, H.; Ali, E.M.; Nusair, K.; Holderbaum, W. Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources. Energies 2021, 14, 2151. https://doi.org/10.3390/en14082151

AMA Style

Alasali F, Foudeh H, Ali EM, Nusair K, Holderbaum W. Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources. Energies. 2021; 14(8):2151. https://doi.org/10.3390/en14082151

Chicago/Turabian Style

Alasali, Feras, Husam Foudeh, Esraa M. Ali, Khaled Nusair, and William Holderbaum. 2021. "Forecasting and Modelling the Uncertainty of Low Voltage Network Demand and the Effect of Renewable Energy Sources" Energies 14, no. 8: 2151. https://doi.org/10.3390/en14082151

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