On the role of building use and operational strategy in integrating ice storage systems: an economic perspective DOI

Saeed Rahgozar,

Abolfazl Pourrajabian, Maziar Dehghan

и другие.

Journal of Energy Storage, Год журнала: 2024, Номер 98, С. 113025 - 113025

Опубликована: Авг. 1, 2024

Язык: Английский

Peak regulation strategies for ground source heat pump demand response of based on load forecasting: A case study of rural building in China DOI
Qinglong Meng, Ying’an Wei,

Jingjing Fan

и другие.

Renewable Energy, Год журнала: 2024, Номер 224, С. 120059 - 120059

Опубликована: Фев. 2, 2024

Язык: Английский

Процитировано

8

A novel building energy consumption prediction method using deep reinforcement learning with consideration of fluctuation points DOI Open Access
Wei Jin, Qiming Fu, Jianping Chen

и другие.

Journal of Building Engineering, Год журнала: 2022, Номер 63, С. 105458 - 105458

Опубликована: Окт. 28, 2022

Язык: Английский

Процитировано

25

Peak-Load Management of Distribution Network Using Conservation Voltage Reduction and Dynamic Thermal Rating DOI Open Access
Ramin Nourollahi, Pouya Salyani, Kazem Zare

и другие.

Sustainability, Год журнала: 2022, Номер 14(18), С. 11569 - 11569

Опубликована: Сен. 15, 2022

The peak-load management of a distribution network (DN) has gained attention by increasing the electric power consumption on demand side. By developing smart-grid infrastructures, effective utilization DN’s components and proper DN would create valuable solution for operators. Hence, in this paper, framework is proposed which real-time rating voltage-dependent features loads help operator handle peak times successfully. In addition to individual advantages efficient operation DN, more practical results are obtained combining conservation voltage reduction (CVR) dynamic thermal (DTR) lines transformers. Based results, compared implementation CVR, cost-saving level increased significantly during events using simultaneous DTR CVR. Furthermore, discussion presented about current problems feeders supplying constant-power CVR utilization, resolved components.

Язык: Английский

Процитировано

23

Energy flexibility of commercial buildings for demand response applications in Australia DOI Creative Commons
Zakia Afroz, Mark Goldsworthy, Stephen D. White

и другие.

Energy and Buildings, Год журнала: 2023, Номер 300, С. 113533 - 113533

Опубликована: Сен. 15, 2023

Demand response (DR) is widely recognized as an important mechanism in the Australian electricity market, though large-scale uptake commercial buildings yet to occur, part due difficulty of characterising resource. This paper describes a bottom-up physics-based approach characterise DR potential three types (schools, offices, and data centres) under global set-point temperature offset strategy. Representative models are calibrated with energy meter parametric analysis used assess sensitivity different building, operating system parameters. Parametric equations provided for relative potentials functions time day. School were found have highest (∼40 - 45%) ambient temperatures over 30°C, followed by centres (∼20 30%) offices (∼20%). Location has strongest influence school office equipment intensity centres. The Australia-wide combined school, estimated be between 551 647 MW. Office aggregate at 1.5 1.7 times that 9 11

Язык: Английский

Процитировано

14

Quantifying the energy flexibility potential of a centralized air-conditioning system: A field test study of hub airports DOI

Ruoyu Xu,

Xiaochen Liu, Xiaohua Liu

и другие.

Energy, Год журнала: 2024, Номер 298, С. 131313 - 131313

Опубликована: Апрель 14, 2024

Язык: Английский

Процитировано

6

Analysis of Peak Demand Reduction and Energy Saving in a Mixed-Use Community through Urban Building Energy Modeling DOI Creative Commons
Wen‐Xian Zhao, Deng Zhang,

Yanfei Ji

и другие.

Energies, Год журнала: 2024, Номер 17(5), С. 1214 - 1214

Опубликована: Март 3, 2024

Energy saving in buildings is essential as buildings’ operational energy use constitutes 30% of global consumption. Urban building modeling (UBEM) effectively understands urban This paper applied UBEM to assess the potential peak demand reduction and a mixed-use community, using 955 residential buildings, 35 office 7 hotels Shenzhen, China, case study. The type period were collected based on GIS dataset. Then, baseline models generated by tool—AutoBPS. Five scenarios analyzed: retrofit-window, retrofit-air conditioner (AC), retrofit-lighting, rooftop photovoltaic (PV), response. five replaced windows, enhanced AC, upgraded lighting, covered 60% roof area with PV, had temperature reset from 17:00 23:00, respectively. results show that retrofit-windows most effective scenario for reducing at 19.09%, PV reduces intensity (EUI) best 29.96%. Demand response recommended when further investment not desired. Retrofit-lighting suggested its low-cost, low-risk investment, payback (PBP) exceeding 4.54 years. When abundant, are public while buildings. research might provide practical insights into policy formulation.

Язык: Английский

Процитировано

4

Identification of cyber-attack/outage/fault in zero-energy building with load and energy management strategies DOI
Reza Hemmati, Hossien Faraji

Journal of Energy Storage, Год журнала: 2022, Номер 50, С. 104290 - 104290

Опубликована: Фев. 28, 2022

Язык: Английский

Процитировано

18

AutoDefect: Defect text classification in residential buildings using a multi-task channel attention network DOI

Donguk Yang,

Byeol Kim,

Sang Hyo Lee

и другие.

Sustainable Cities and Society, Год журнала: 2022, Номер 80, С. 103803 - 103803

Опубликована: Фев. 25, 2022

Язык: Английский

Процитировано

18

Towards Sustainable Energy Use: Reinforcement Learning for Demand Response in Commercial Buildings DOI Creative Commons
Seyyedreza Madani, Pierre‐Olivier Pineau, Laurent Charlin

и другие.

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115721 - 115721

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Review of Optimization Control Methods for HVAC Systems in Demand Response (DR): Transition from Model-driven to Model-free Approaches and Challenges DOI

Ruiying Jin,

Peng Xu, Jiefan Gu

и другие.

Building and Environment, Год журнала: 2025, Номер unknown, С. 113045 - 113045

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0