Data enabled predictive energy management of a PV-battery smart home nanogrid

Chao Sun; Fengchun Sun; Scott J. Moura

doi:10.1109/ACC.2015.7170867

Data enabled predictive energy management of a PV-battery smart home nanogrid

Chao Sun, Fengchun Sun, Scott J. Moura

School of Mechanical Engineering

University of California at Berkeley

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

24 Citations (Scopus)

Abstract

This paper proposes a data-enabled predictive energy management strategy for a smart home nanogrid (NG) that includes a photovoltaic system and second-life battery energy storage. The key novelty is utilizing data-based forecasts of future load demand, weather conditions, electricity price, and power plant CO₂ emissions to improve the NG system efficiency. Specifically, a load demand forecast model is developed using an artificial neural network (ANN). The forecast model predicts load demand signals for a model predictive controller (MPC). Simulation results show that the data-enabled predictive energy management strategy achieves 96%-98% of the optimal NG performance derived via dynamic programming (DP). Its sensitivity to the control horizon length and load demand forecast accuracy are also investigated.

Original language	English
Title of host publication	ACC 2015 - 2015 American Control Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1023-1028
Number of pages	6
ISBN (Electronic)	9781479986842
DOIs	https://doi.org/10.1109/ACC.2015.7170867
Publication status	Published - 28 Jul 2015
Event	2015 American Control Conference, ACC 2015 - Chicago, United States Duration: 1 Jul 2015 → 3 Jul 2015

Publication series

Name	Proceedings of the American Control Conference
Volume	2015-July
ISSN (Print)	0743-1619

Conference

Conference	2015 American Control Conference, ACC 2015
Country/Territory	United States
City	Chicago
Period	1/07/15 → 3/07/15

UN SDGs

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

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10.1109/ACC.2015.7170867

Cite this

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title = "Data enabled predictive energy management of a PV-battery smart home nanogrid",

abstract = "This paper proposes a data-enabled predictive energy management strategy for a smart home nanogrid (NG) that includes a photovoltaic system and second-life battery energy storage. The key novelty is utilizing data-based forecasts of future load demand, weather conditions, electricity price, and power plant CO2 emissions to improve the NG system efficiency. Specifically, a load demand forecast model is developed using an artificial neural network (ANN). The forecast model predicts load demand signals for a model predictive controller (MPC). Simulation results show that the data-enabled predictive energy management strategy achieves 96%-98% of the optimal NG performance derived via dynamic programming (DP). Its sensitivity to the control horizon length and load demand forecast accuracy are also investigated.",

author = "Chao Sun and Fengchun Sun and Moura, {Scott J.}",

note = "Publisher Copyright: {\textcopyright} 2015 American Automatic Control Council.; 2015 American Control Conference, ACC 2015 ; Conference date: 01-07-2015 Through 03-07-2015",

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doi = "10.1109/ACC.2015.7170867",

language = "English",

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Sun, C , Sun, F & Moura, SJ 2015, Data enabled predictive energy management of a PV-battery smart home nanogrid. in ACC 2015 - 2015 American Control Conference., 7170867, Proceedings of the American Control Conference, vol. 2015-July, Institute of Electrical and Electronics Engineers Inc., pp. 1023-1028, 2015 American Control Conference, ACC 2015, Chicago, United States, 1/07/15. https://doi.org/10.1109/ACC.2015.7170867

Data enabled predictive energy management of a PV-battery smart home nanogrid. / Sun, Chao ; Sun, Fengchun; Moura, Scott J.
ACC 2015 - 2015 American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1023-1028 7170867 (Proceedings of the American Control Conference; Vol. 2015-July).

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

TY - GEN

T1 - Data enabled predictive energy management of a PV-battery smart home nanogrid

AU - Sun, Chao

AU - Sun, Fengchun

AU - Moura, Scott J.

PY - 2015/7/28

Y1 - 2015/7/28

N2 - This paper proposes a data-enabled predictive energy management strategy for a smart home nanogrid (NG) that includes a photovoltaic system and second-life battery energy storage. The key novelty is utilizing data-based forecasts of future load demand, weather conditions, electricity price, and power plant CO2 emissions to improve the NG system efficiency. Specifically, a load demand forecast model is developed using an artificial neural network (ANN). The forecast model predicts load demand signals for a model predictive controller (MPC). Simulation results show that the data-enabled predictive energy management strategy achieves 96%-98% of the optimal NG performance derived via dynamic programming (DP). Its sensitivity to the control horizon length and load demand forecast accuracy are also investigated.

AB - This paper proposes a data-enabled predictive energy management strategy for a smart home nanogrid (NG) that includes a photovoltaic system and second-life battery energy storage. The key novelty is utilizing data-based forecasts of future load demand, weather conditions, electricity price, and power plant CO2 emissions to improve the NG system efficiency. Specifically, a load demand forecast model is developed using an artificial neural network (ANN). The forecast model predicts load demand signals for a model predictive controller (MPC). Simulation results show that the data-enabled predictive energy management strategy achieves 96%-98% of the optimal NG performance derived via dynamic programming (DP). Its sensitivity to the control horizon length and load demand forecast accuracy are also investigated.

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SP - 1023

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BT - ACC 2015 - 2015 American Control Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2015 American Control Conference, ACC 2015

Y2 - 1 July 2015 through 3 July 2015

ER -

Data enabled predictive energy management of a PV-battery smart home nanogrid

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UN SDGs

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