ORCID Profile
0000-0003-1115-4200
Current Organisation
University of Adelaide
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: IEEE
Date: 26-09-2022
Publisher: IEEE
Date: 09-2012
Publisher: IEEE
Date: 26-09-2022
Publisher: ACTAPRESS
Date: 2012
Publisher: IEEE
Date: 07-2018
Publisher: IEEE
Date: 05-2019
Publisher: IEEE
Date: 2011
Publisher: IEEE
Date: 10-2007
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 09-2022
Publisher: Elsevier BV
Date: 02-2023
Publisher: MDPI AG
Date: 07-01-2016
DOI: 10.3390/EN9010034
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Publisher: IEEE
Date: 09-2012
Publisher: IEEE
Date: 11-2018
Publisher: Elsevier BV
Date: 04-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
Publisher: IEEE
Date: 2012
Publisher: IEEE
Date: 09-2013
Publisher: IEEE
Date: 11-2020
Publisher: Elsevier BV
Date: 10-2021
Publisher: Elsevier BV
Date: 04-2018
Publisher: IEEE
Date: 07-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2014
Publisher: IEEE
Date: 05-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2010
Publisher: Elsevier BV
Date: 11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Elsevier BV
Date: 02-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Publisher: Institution of Engineering and Technology (IET)
Date: 2021
DOI: 10.1049/RPG2.12008
Publisher: IEEE
Date: 06-2018
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 2023
Publisher: Elsevier BV
Date: 05-2018
Publisher: IEEE
Date: 09-2016
Publisher: Elsevier BV
Date: 2011
Publisher: Institution of Engineering and Technology
Date: 29-07-2019
DOI: 10.1049/PBPO139E_CH2
Publisher: ACM
Date: 28-06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2012
Publisher: Elsevier BV
Date: 02-2023
Publisher: IEEE
Date: 05-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Publisher: Elsevier BV
Date: 02-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 10-2008
Publisher: IEEE
Date: 06-2018
Publisher: IEEE
Date: 08-2011
Publisher: IEEE
Date: 09-2016
Publisher: Springer Science and Business Media LLC
Date: 08-06-2023
DOI: 10.1038/S41597-023-02271-3
Abstract: Conventional residential electricity consumers are becoming prosumers who not only consume electricity but also produce it. This shift is expected to occur over the next few decades at a large scale, and it presents numerous uncertainties and risks for the operation, planning, investment, and viable business models of the electricity grid. To prepare for this shift, researchers, utilities, policymakers, and emerging businesses require a comprehensive understanding of future prosumers’ electricity consumption. Unfortunately, there is a limited amount of data available due to privacy concerns and the slow adoption of new technologies such as battery electric vehicles and home automation. To address this issue, this paper introduces a synthetic dataset containing five types of residential prosumers’ imported and exported electricity data. The dataset was developed using real traditional consumers’ data from Denmark, PV generation data from the global solar energy estimator (GSEE) model, electric vehicle (EV) charging data generated using package, a residential energy storage system (ESS) operator and a generative adversarial network (GAN) based model to produce synthetic data. The quality of the dataset was assessed and validated through qualitative inspection and three methods: empirical statistics, metrics based on information theory, and evaluation metrics based on machine learning techniques.
Publisher: Informa UK Limited
Date: 09-03-2011
Location: Iran (Islamic Republic of)
No related grants have been discovered for Seyyed Ali Pourmousavi Kani.