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
N1 - Publisher Copyright:
© 2018 IEEE.
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.
UR - http://www.scopus.com/inward/record.url?scp=85053855540&partnerID=8YFLogxK
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U2 - 10.1109/PEDG.2018.8447834
DO - 10.1109/PEDG.2018.8447834
M3 - Conference contribution
AN - SCOPUS:85053855540
SN - 9781538667057
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.
T2 - 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018
Y2 - 25 June 2018 through 28 June 2018
ER -
