Quantifying and Improving Building Energy Flexibility with Coordinated PV-Battery Systems: A Data-Driven Model-Based Approach

Zesheng Yang; Dezhou Kong; Zhexuan Chen; Yude Fu; Haocheng Ma; Ziwei Yin; Zhiang Zhang; Guozhao Gao

doi:10.1007/978-3-032-11910-0_26

Quantifying and Improving Building Energy Flexibility with Coordinated PV-Battery Systems: A Data-Driven Model-Based Approach

Zesheng Yang^*
, Dezhou Kong
, Zhexuan Chen
, Yude Fu
, Haocheng Ma
, Ziwei Yin
, Zhiang Zhang
, Guozhao Gao

^*Corresponding author for this work

Department of Architecture and Built Environment

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

Abstract

Building energy flexibility is critical for integrating renewables and achieving efficient energy management. This study develops a multi-indicator assessment framework and a customized battery storage regulation strategy to improve flexibility in PV-integrated buildings. LSTM and Rolling-XGB models are employed to accurately predict building energy demand and PV generation, respectively. A data-driven battery model simulates system performance under three scenarios: standalone PV, uncoordinated PV-BESS, and coordinated PV-BESS with forecasting. Validation is conducted on a near-zero-energy building in Ningbo Zhejiang. Results show that the coordinated strategy significantly enhances flexibility and reduces energy costs by nearly 40% compared to the PV-only case. The system achieves 62.75% local energy coverage and 34.77% energy surplus time percentage, demonstrating the effectiveness of the proposed approach in balancing supply and demand.

Original language	English
Title of host publication	Climate Resilience and Data Forecasting - Selected Papers of 2025 International Conference on Climate-Resilient and Low-Carbon Cities
Editors	Bao-Jie He, Deo Prasad, Ali Cheshmehzangi, Ming Ma
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	323-331
Number of pages	9
ISBN (Print)	9783032119094
DOIs	https://doi.org/10.1007/978-3-032-11910-0_26
Publication status	Published - 25 Jan 2026
Event	International Conference on Climate-resilient and Low-carbon Cities, iCCRLCC 2025 - Changzhou, China Duration: 22 Aug 2025 → 24 Aug 2025

Publication series

Name	Environmental Science and Engineering
ISSN (Print)	1863-5520
ISSN (Electronic)	1863-5539

Conference

Conference	International Conference on Climate-resilient and Low-carbon Cities, iCCRLCC 2025
Country/Territory	China
City	Changzhou
Period	22/08/25 → 24/08/25

UN SDGs

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

SDG 7 Affordable and Clean Energy

Free Keywords

Battery energy storage system (BESS)
Building energy flexibility
Building integrated photovoltaics
Data-driven model

ASJC Scopus subject areas

Environmental Engineering
Information Systems

Access to Document

10.1007/978-3-032-11910-0_26

Cite this

Yang, Z., Kong, D., Chen, Z., Fu, Y., Ma, H., Yin, Z., Zhang, Z., & Gao, G. (2026). Quantifying and Improving Building Energy Flexibility with Coordinated PV-Battery Systems: A Data-Driven Model-Based Approach. In B.-J. He, D. Prasad, A. Cheshmehzangi, & M. Ma (Eds.), Climate Resilience and Data Forecasting - Selected Papers of 2025 International Conference on Climate-Resilient and Low-Carbon Cities (pp. 323-331). (Environmental Science and Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-11910-0_26

Yang, Zesheng ; Kong, Dezhou ; Chen, Zhexuan et al. / Quantifying and Improving Building Energy Flexibility with Coordinated PV-Battery Systems : A Data-Driven Model-Based Approach. Climate Resilience and Data Forecasting - Selected Papers of 2025 International Conference on Climate-Resilient and Low-Carbon Cities. editor / Bao-Jie He ; Deo Prasad ; Ali Cheshmehzangi ; Ming Ma. Springer Science and Business Media Deutschland GmbH, 2026. pp. 323-331 (Environmental Science and Engineering).

@inproceedings{e3ad968f1fd64025b6bcf588406c44da,

title = "Quantifying and Improving Building Energy Flexibility with Coordinated PV-Battery Systems: A Data-Driven Model-Based Approach",

abstract = "Building energy flexibility is critical for integrating renewables and achieving efficient energy management. This study develops a multi-indicator assessment framework and a customized battery storage regulation strategy to improve flexibility in PV-integrated buildings. LSTM and Rolling-XGB models are employed to accurately predict building energy demand and PV generation, respectively. A data-driven battery model simulates system performance under three scenarios: standalone PV, uncoordinated PV-BESS, and coordinated PV-BESS with forecasting. Validation is conducted on a near-zero-energy building in Ningbo Zhejiang. Results show that the coordinated strategy significantly enhances flexibility and reduces energy costs by nearly 40\% compared to the PV-only case. The system achieves 62.75\% local energy coverage and 34.77\% energy surplus time percentage, demonstrating the effectiveness of the proposed approach in balancing supply and demand.",

keywords = "Battery energy storage system (BESS), Building energy flexibility, Building integrated photovoltaics, Data-driven model",

author = "Zesheng Yang and Dezhou Kong and Zhexuan Chen and Yude Fu and Haocheng Ma and Ziwei Yin and Zhiang Zhang and Guozhao Gao",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; International Conference on Climate-resilient and Low-carbon Cities, iCCRLCC 2025 ; Conference date: 22-08-2025 Through 24-08-2025",

year = "2026",

month = jan,

day = "25",

doi = "10.1007/978-3-032-11910-0\_26",

language = "English",

isbn = "9783032119094",

series = "Environmental Science and Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "323--331",

editor = "Bao-Jie He and Deo Prasad and Ali Cheshmehzangi and Ming Ma",

booktitle = "Climate Resilience and Data Forecasting - Selected Papers of 2025 International Conference on Climate-Resilient and Low-Carbon Cities",

address = "Germany",

}

Yang, Z, Kong, D, Chen, Z, Fu, Y, Ma, H, Yin, Z, Zhang, Z & Gao, G 2026, Quantifying and Improving Building Energy Flexibility with Coordinated PV-Battery Systems: A Data-Driven Model-Based Approach. in B-J He, D Prasad, A Cheshmehzangi & M Ma (eds), Climate Resilience and Data Forecasting - Selected Papers of 2025 International Conference on Climate-Resilient and Low-Carbon Cities. Environmental Science and Engineering, Springer Science and Business Media Deutschland GmbH, pp. 323-331, International Conference on Climate-resilient and Low-carbon Cities, iCCRLCC 2025, Changzhou, China, 22/08/25. https://doi.org/10.1007/978-3-032-11910-0_26

Quantifying and Improving Building Energy Flexibility with Coordinated PV-Battery Systems: A Data-Driven Model-Based Approach. / Yang, Zesheng; Kong, Dezhou; Chen, Zhexuan et al.
Climate Resilience and Data Forecasting - Selected Papers of 2025 International Conference on Climate-Resilient and Low-Carbon Cities. ed. / Bao-Jie He; Deo Prasad; Ali Cheshmehzangi; Ming Ma. Springer Science and Business Media Deutschland GmbH, 2026. p. 323-331 (Environmental Science and Engineering).

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

TY - GEN

T1 - Quantifying and Improving Building Energy Flexibility with Coordinated PV-Battery Systems

T2 - International Conference on Climate-resilient and Low-carbon Cities, iCCRLCC 2025

AU - Yang, Zesheng

AU - Kong, Dezhou

AU - Chen, Zhexuan

AU - Fu, Yude

AU - Ma, Haocheng

AU - Yin, Ziwei

AU - Zhang, Zhiang

AU - Gao, Guozhao

PY - 2026/1/25

Y1 - 2026/1/25

N2 - Building energy flexibility is critical for integrating renewables and achieving efficient energy management. This study develops a multi-indicator assessment framework and a customized battery storage regulation strategy to improve flexibility in PV-integrated buildings. LSTM and Rolling-XGB models are employed to accurately predict building energy demand and PV generation, respectively. A data-driven battery model simulates system performance under three scenarios: standalone PV, uncoordinated PV-BESS, and coordinated PV-BESS with forecasting. Validation is conducted on a near-zero-energy building in Ningbo Zhejiang. Results show that the coordinated strategy significantly enhances flexibility and reduces energy costs by nearly 40% compared to the PV-only case. The system achieves 62.75% local energy coverage and 34.77% energy surplus time percentage, demonstrating the effectiveness of the proposed approach in balancing supply and demand.

AB - Building energy flexibility is critical for integrating renewables and achieving efficient energy management. This study develops a multi-indicator assessment framework and a customized battery storage regulation strategy to improve flexibility in PV-integrated buildings. LSTM and Rolling-XGB models are employed to accurately predict building energy demand and PV generation, respectively. A data-driven battery model simulates system performance under three scenarios: standalone PV, uncoordinated PV-BESS, and coordinated PV-BESS with forecasting. Validation is conducted on a near-zero-energy building in Ningbo Zhejiang. Results show that the coordinated strategy significantly enhances flexibility and reduces energy costs by nearly 40% compared to the PV-only case. The system achieves 62.75% local energy coverage and 34.77% energy surplus time percentage, demonstrating the effectiveness of the proposed approach in balancing supply and demand.

KW - Battery energy storage system (BESS)

KW - Building energy flexibility

KW - Building integrated photovoltaics

KW - Data-driven model

UR - https://www.scopus.com/pages/publications/105028952119

U2 - 10.1007/978-3-032-11910-0_26

DO - 10.1007/978-3-032-11910-0_26

M3 - Conference contribution

AN - SCOPUS:105028952119

SN - 9783032119094

T3 - Environmental Science and Engineering

SP - 323

EP - 331

BT - Climate Resilience and Data Forecasting - Selected Papers of 2025 International Conference on Climate-Resilient and Low-Carbon Cities

A2 - He, Bao-Jie

A2 - Prasad, Deo

A2 - Cheshmehzangi, Ali

A2 - Ma, Ming

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 22 August 2025 through 24 August 2025

ER -

Yang Z, Kong D, Chen Z, Fu Y, Ma H, Yin Z et al. Quantifying and Improving Building Energy Flexibility with Coordinated PV-Battery Systems: A Data-Driven Model-Based Approach. In He BJ, Prasad D, Cheshmehzangi A, Ma M, editors, Climate Resilience and Data Forecasting - Selected Papers of 2025 International Conference on Climate-Resilient and Low-Carbon Cities. Springer Science and Business Media Deutschland GmbH. 2026. p. 323-331. (Environmental Science and Engineering). doi: 10.1007/978-3-032-11910-0_26

Quantifying and Improving Building Energy Flexibility with Coordinated PV-Battery Systems: A Data-Driven Model-Based Approach

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

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