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

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

5 Citations (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

Original language	English
Title of host publication	Proceedings of 2018 2nd European Conference on Electrical Engineering and Computer Science (EECS)
Publisher	IEEE Press
Publication date	Dec 2018
ISBN (Electronic)	978-1-7281-1929-8
DOIs	https://doi.org/10.1109/EECS.2018.00091
Publication status	Published - Dec 2018
Event	2018 2nd European Conference on Electrical Engineering and Computer Science (EECS) - Bern, Switzerland Duration: 20 Dec 2018 → 22 Dec 2018

Conference

Conference	2018 2nd European Conference on Electrical Engineering and Computer Science (EECS)
Country/Territory	Switzerland
City	Bern
Period	20/12/2018 → 22/12/2018

Keywords

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

Access to Document

10.1109/EECS.2018.00091Licence: Unspecified

1 Finished

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. & N. Soltani, M.
ELFORSK
05/02/2018 → 01/09/2020
Project: Research

5 Citations
1 Ph.D. thesis

Energy Management System for Smart Homes: Modeling, Control, Performance and Profit Assessment
Yousefi, M., 2020, Aalborg Universitetsforlag. 61 p. (Ph.d.-serien for Det Ingeniør- og Naturvidenskabelige Fakultet, Aalborg Universitet).
Research output: Book/Report › Ph.D. thesis

Open Access
File

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

File

Cite this

@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. in 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, Switzerland, 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

Conference

Keywords

Access to Document

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Projects

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

Research output

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

Datasets

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

Cite this