ANFIS Based Approach for Stochastic Modeling of Smart Home

Mojtaba Yousefi; Nasrin Kianpoor; Amin Hajizadeh; Mohsen N. Soltani

doi:10.1109/EECS.2018.00091

ANFIS Based Approach for Stochastic Modeling of Smart Home

Mojtaba Yousefi, Nasrin Kianpoor, Amin Hajizadeh, Mohsen N. Soltani

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

5 Citationer (Scopus)

Abstract

Designing a proper energy management system for a smart home is crucial to monitor, control and optimize the flow and use of energy. The energy management system is highly dependent on a well-developed and accurate model of the smart home components. In this paper, the stochastic characteristics and uncertainties of the smart home components including photovoltaic, plug-in electric vehicle and heat pump are taken into account to develop a stochastic model. Hence, forecasting models are developed for photovoltaic power generation and load demand by the adaptive neuro-fuzzy inference system. Moreover, a Markov chain is proposed to model the trip time of the plugin electric vehicle model and a conditional probability model is also employed for calculation of battery energy at the plug-in time. Finally, the performance of the proposed stochastic model is compared with a neural

Originalsprog	Engelsk
Titel	Proceedings of 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS)
Forlag	IEEE Press
Publikationsdato	dec. 2018
ISBN (Elektronisk)	978-1-7281-1929-8
DOI	https://doi.org/10.1109/EECS.2018.00091
Status	Udgivet - dec. 2018
Begivenhed	2018 2nd European Conference on Electrical Engineering and Computer Science (EECS) - Bern, Schweiz Varighed: 20 dec. 2018 → 22 dec. 2018

Konference

Konference	2018 2nd European Conference on Electrical Engineering and Computer Science (EECS)
Land/Område	Schweiz
By	Bern
Periode	20/12/2018 → 22/12/2018

Adgang til dokumentet

10.1109/EECS.2018.00091Licens: Ikke-specificeret

AUB Link

Søg efter materialet i Aalborg Universitetsbiblioteks søgemaskine

1 Afsluttet

Energy management in a "smart home" with integrated solar cells, electric vehicles and heat pumps - Energistyring i et ”smart home” med integrerede solceller, elbiler og varmepumper
Hajizadeh, A. (PI (principal investigator)) & N. Soltani, M. (CoI (co-investigator))
ELFORSK
05/02/2018 → 01/09/2020
Projekter: Projekt › Forskning

5 Citationer
1 Ph.d.-afhandling

Energy Management System for Smart Homes: Modeling, Control, Performance and Profit Assessment
Yousefi, M., 2020, Aalborg Universitetsforlag. 61 s.
Publikation: Ph.d.-afhandling

Åben adgang
Fil

Wind-Solar Measurement Database for Renewable Energy Control Laboratory at Aalborg University Esbjerg
Kristoffersen, K. C. (Ophavsperson), N. Soltani, M. (Ophavsperson), Hajizadeh, A. (Ophavsperson), Bjørn, P. (Ophavsperson) & Enevoldsen, H. (Ophavsperson), Aalborg Universitet, 26 sep. 2019
Datasæt

Fil

Citationsformater

@inproceedings{ca69c6653f0948768d1d208bc983f38d,

title = "ANFIS Based Approach for Stochastic Modeling of Smart Home",

abstract = "Designing a proper energy management system for a smart home is crucial to monitor, control and optimize the flow and use of energy. The energy management system is highly dependent on a well-developed and accurate model of the smart home components. In this paper, the stochastic characteristics and uncertainties of the smart home components including photovoltaic, plug-in electric vehicle and heat pump are taken into account to develop a stochastic model. Hence, forecasting models are developed for photovoltaic power generation and load demand by the adaptive neuro-fuzzy inference system. Moreover, a Markov chain is proposed to model the trip time of the plugin electric vehicle model and a conditional probability model is also employed for calculation of battery energy at the plug-in time. Finally, the performance of the proposed stochastic model is compared with a neural",

keywords = "Smart home, Plug-in electric vehicle, Adaptive Neuro-Fuzzy inference system, Stochastic Modeling, Photovoltaic Array, Heat Pump",

author = "Mojtaba Yousefi and Nasrin Kianpoor and Amin Hajizadeh and {N. Soltani}, Mohsen",

year = "2018",

month = dec,

doi = "10.1109/EECS.2018.00091",

language = "English",

booktitle = "Proceedings of 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS)",

publisher = "IEEE Press",

note = "2018 2nd European Conference on Electrical Engineering and Computer Science (EECS) ; Conference date: 20-12-2018 Through 22-12-2018",

}

Yousefi, M, Kianpoor, N, Hajizadeh, A & N. Soltani, M 2018, ANFIS Based Approach for Stochastic Modeling of Smart Home. i Proceedings of 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS). IEEE Press, 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS), Bern, Schweiz, 20/12/2018. https://doi.org/10.1109/EECS.2018.00091

TY - GEN

T1 - ANFIS Based Approach for Stochastic Modeling of Smart Home

AU - Yousefi, Mojtaba

AU - Kianpoor, Nasrin

AU - Hajizadeh, Amin

AU - N. Soltani, Mohsen

PY - 2018/12

Y1 - 2018/12

N2 - Designing a proper energy management system for a smart home is crucial to monitor, control and optimize the flow and use of energy. The energy management system is highly dependent on a well-developed and accurate model of the smart home components. In this paper, the stochastic characteristics and uncertainties of the smart home components including photovoltaic, plug-in electric vehicle and heat pump are taken into account to develop a stochastic model. Hence, forecasting models are developed for photovoltaic power generation and load demand by the adaptive neuro-fuzzy inference system. Moreover, a Markov chain is proposed to model the trip time of the plugin electric vehicle model and a conditional probability model is also employed for calculation of battery energy at the plug-in time. Finally, the performance of the proposed stochastic model is compared with a neural

AB - Designing a proper energy management system for a smart home is crucial to monitor, control and optimize the flow and use of energy. The energy management system is highly dependent on a well-developed and accurate model of the smart home components. In this paper, the stochastic characteristics and uncertainties of the smart home components including photovoltaic, plug-in electric vehicle and heat pump are taken into account to develop a stochastic model. Hence, forecasting models are developed for photovoltaic power generation and load demand by the adaptive neuro-fuzzy inference system. Moreover, a Markov chain is proposed to model the trip time of the plugin electric vehicle model and a conditional probability model is also employed for calculation of battery energy at the plug-in time. Finally, the performance of the proposed stochastic model is compared with a neural

KW - Smart home

KW - Plug-in electric vehicle

KW - Adaptive Neuro-Fuzzy inference system

KW - Stochastic Modeling, Photovoltaic Array

KW - Heat Pump

U2 - 10.1109/EECS.2018.00091

DO - 10.1109/EECS.2018.00091

M3 - Article in proceeding

BT - Proceedings of 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS)

PB - IEEE Press

T2 - 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS)

Y2 - 20 December 2018 through 22 December 2018

ER -

ANFIS Based Approach for Stochastic Modeling of Smart Home

Abstract

Konference

Adgang til dokumentet

AUB Link

Fingeraftryk

Projekter

Energy management in a "smart home" with integrated solar cells, electric vehicles and heat pumps - Energistyring i et ”smart home” med integrerede solceller, elbiler og varmepumper

Publikation

Energy Management System for Smart Homes: Modeling, Control, Performance and Profit Assessment

Forskningsdatasæt

Wind-Solar Measurement Database for Renewable Energy Control Laboratory at Aalborg University Esbjerg

Citationsformater