Power Management and Optimization for a Residential Smart Microgrid Using Stochastic Methods

DIba Zia Amirhosseini; Reza Sabzehaar; Mohammad Rasouli

doi:10.1109/PEDG.2018.8447834

Power Management and Optimization for a Residential Smart Microgrid Using Stochastic Methods

DIba Zia Amirhosseini
, Reza Sabzehaar
, Mohammad Rasouli

School of Engineering (Behrend)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Scopus citations

Abstract

In this paper, energy usage and its associated price for a residential smart microgrid are analyzed using three different forecasting methods: Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Machines (SVM), and Polynomial Regression. Energy demand and its price are then forecast while taking into account the effect of demand response. The accuracy of the forecast values are evaluated using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) criteria. Numerical results, based on the data acquired for a residential microgrid from San Diego Gas Electric (SDGE), are used for model validation purposes. Such data are employed to assess the performance, demonstrate the effectiveness and verify the reliability of the proposed optimization and forecasting methods. Our analyses show that the ARIMA method is more accurate in forecasting the demand as well as the price of energy for the smart migrogrid compared to other methods.

Original language	English (US)
Title of host publication	2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781538667057
DOIs	https://doi.org/10.1109/PEDG.2018.8447834
State	Published - Aug 27 2018
Event	9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018 - Charlotte, United States Duration: Jun 25 2018 → Jun 28 2018

Publication series

Name	2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018

Other

Other	9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018
Country/Territory	United States
City	Charlotte
Period	6/25/18 → 6/28/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

All Science Journal Classification (ASJC) codes

Energy Engineering and Power Technology
Electrical and Electronic Engineering
Renewable Energy, Sustainability and the Environment
Control and Optimization

Access to Document

10.1109/PEDG.2018.8447834

Cite this

Amirhosseini, DI. Z., Sabzehaar, R., & Rasouli, M. (2018). Power Management and Optimization for a Residential Smart Microgrid Using Stochastic Methods. In 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018 Article 8447834 (2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PEDG.2018.8447834

Amirhosseini, DIba Zia ; Sabzehaar, Reza ; Rasouli, Mohammad. / Power Management and Optimization for a Residential Smart Microgrid Using Stochastic Methods. 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018. Institute of Electrical and Electronics Engineers Inc., 2018. (2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018).

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title = "Power Management and Optimization for a Residential Smart Microgrid Using Stochastic Methods",

abstract = "In this paper, energy usage and its associated price for a residential smart microgrid are analyzed using three different forecasting methods: Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Machines (SVM), and Polynomial Regression. Energy demand and its price are then forecast while taking into account the effect of demand response. The accuracy of the forecast values are evaluated using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) criteria. Numerical results, based on the data acquired for a residential microgrid from San Diego Gas Electric (SDGE), are used for model validation purposes. Such data are employed to assess the performance, demonstrate the effectiveness and verify the reliability of the proposed optimization and forecasting methods. Our analyses show that the ARIMA method is more accurate in forecasting the demand as well as the price of energy for the smart migrogrid compared to other methods.",

author = "Amirhosseini, \{DIba Zia\} and Reza Sabzehaar and Mohammad Rasouli",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018 ; Conference date: 25-06-2018 Through 28-06-2018",

year = "2018",

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day = "27",

doi = "10.1109/PEDG.2018.8447834",

language = "English (US)",

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series = "2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018",

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Amirhosseini, DIZ, Sabzehaar, R & Rasouli, M 2018, Power Management and Optimization for a Residential Smart Microgrid Using Stochastic Methods. in 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018., 8447834, 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018, Institute of Electrical and Electronics Engineers Inc., 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018, Charlotte, United States, 6/25/18. https://doi.org/10.1109/PEDG.2018.8447834

Power Management and Optimization for a Residential Smart Microgrid Using Stochastic Methods. / Amirhosseini, DIba Zia; Sabzehaar, Reza; Rasouli, Mohammad.
2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8447834 (2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Power Management and Optimization for a Residential Smart Microgrid Using Stochastic Methods

AU - Amirhosseini, DIba Zia

AU - Sabzehaar, Reza

AU - Rasouli, Mohammad

PY - 2018/8/27

Y1 - 2018/8/27

N2 - In this paper, energy usage and its associated price for a residential smart microgrid are analyzed using three different forecasting methods: Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Machines (SVM), and Polynomial Regression. Energy demand and its price are then forecast while taking into account the effect of demand response. The accuracy of the forecast values are evaluated using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) criteria. Numerical results, based on the data acquired for a residential microgrid from San Diego Gas Electric (SDGE), are used for model validation purposes. Such data are employed to assess the performance, demonstrate the effectiveness and verify the reliability of the proposed optimization and forecasting methods. Our analyses show that the ARIMA method is more accurate in forecasting the demand as well as the price of energy for the smart migrogrid compared to other methods.

AB - In this paper, energy usage and its associated price for a residential smart microgrid are analyzed using three different forecasting methods: Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Machines (SVM), and Polynomial Regression. Energy demand and its price are then forecast while taking into account the effect of demand response. The accuracy of the forecast values are evaluated using Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) criteria. Numerical results, based on the data acquired for a residential microgrid from San Diego Gas Electric (SDGE), are used for model validation purposes. Such data are employed to assess the performance, demonstrate the effectiveness and verify the reliability of the proposed optimization and forecasting methods. Our analyses show that the ARIMA method is more accurate in forecasting the demand as well as the price of energy for the smart migrogrid compared to other methods.

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M3 - Conference contribution

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T3 - 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018

BT - 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018

PB - Institute of Electrical and Electronics Engineers Inc.

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Amirhosseini DIZ, Sabzehaar R, Rasouli M. Power Management and Optimization for a Residential Smart Microgrid Using Stochastic Methods. In 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8447834. (2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018). doi: 10.1109/PEDG.2018.8447834

Power Management and Optimization for a Residential Smart Microgrid Using Stochastic Methods

Abstract

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Other

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

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