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Your search keyword '"Yi Zhang"' showing total 4 results
Start Over Author "Yi Zhang" Region shenzhen (guangdong sheng, china : east)
4 results on '"Yi Zhang"'

1. Electric Bus Scheduling Considering Limited Charging Facility Capacity for Large-Scale Operation.

Author

Mengyan Jiang and Yi Zhang

Subjects
*ELECTRIC motor buses, *URBAN transit systems, *OPERATING costs, *CONGESTION pricing, *SCHEDULING
Abstract

Electric buses (e-buses) have been increasingly introduced to urban transit systems in recent years. E-bus scheduling has become a critical problem to ensure cost-efficient transit service. To support continuous e-bus operation, charging facilities are established at the depots to provide charging service. In real-world scenarios, charging facility capacity measured by the number of chargers is always limited. To reduce charging congestions, it is critical to consider the charging facility capacity in the e-bus scheduling problem. In this study, we addressed a multidepot e-bus scheduling problem considering limited charging facility capacity and vehicle-depot constraint. A mixed-integer programming model based on discrete-event optimization method and a large neighborhood search (LNS) heuristic are proposed to solve the problem. Comprehensive numerical experiments based on the real-world operation cases in Shenzhen were conducted. The results verify the model and show that the LNS heuristic can generate near-optimal solutions for large-scale problem instances. Lack of charging facilities at the depots can lead to more e-buses charging at the peak time regarding the time-of-use tariff, resulting in increased total operational cost. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Generation of Load Profile Based on GAN and LSTM Network in Smart Grid.

Author

Yuhang Zhang, Xiangtian Deng, and Yi Zhang

Subjects
*MACHINE learning, *SHORT-term memory, *LONG-term memory, *SMART meters, *GENERATIVE adversarial networks, *ARTIFICIAL intelligence
Abstract

With the development of artificial intelligence technology in recent years, more and more researchers apply these technologies to the related research of smart grid. However, machine learning models are essentially data-driven models, which are needed to be trained on a great amount of data, and then they are able to predict and analyze new data. However, in the process of acquiring smart meter data through transmission system, there are many problems, such as time consuming, high smart meter deployment cost, and data privacy concern. These problems make it difficult to obtain load data, which hinders the research and application of data-driven methods in the smart grid field. Therefore, generating load profiles is a promising solution. In this study, we propose Long Short Term Memory Generative Adversarial Network (LSTMGAN) model to generate load profile. GAN module is used to generate load profiles. The trained LSTM prediction network serves as supervision module. It considers the difference between latent features of generated load profile and real load profile as feature loss. The latent series representation has been improved by adding the feature loss to the original generator loss. The experiment is conducted on load profile of office building and commercial building in Shenzhen. We evaluate the proposed model from similarity, variability and diversity. The result shows that LSTMGAN model can generate load profiles which can reflect not only the real load trend but also stochastic behavior and perform better than traditional GAN and VAE in load profile generation. [ABSTRACT FROM AUTHOR]

Published
2023

3. Evaluation of Life Cycle Carbon Emission Under Energy Flexibility Strategy: A Case Study of Residential Buildings in Shenzhen, China.

Author

Yingyue Li, Rui Miao, Yi Zhang, and He Qi

Subjects
*CARBON emissions, *CARBON cycle, *HEAT storage, *ELECTRIC vehicles, *SKYSCRAPERS, *BATTERY storage plants, *CARBON offsetting, *SUBWAYS
Abstract

The ambitious environmental goal set by Chinese government of reaching peak carbon emission by 2030 and carbon neutrality by 2060 have aroused attention of zero carbon strategy development by different sectors. Among them, the life-cycle carbon emission of building sector accounts for approximate half of the total emissions. The flexible operation of building energy system is a promising way to balance the instantaneous demand and supply, which can improve the reliability of the renewable energy supply system. Under this strategy, solar photovoltaic (PV) and energy storage are considered as important built-in flexibility resources for buildings to reduce operation carbon emission. However, there is lack of quantitative analysis to investigate the effect of these flexibility resources on life-cycle carbon emission of buildings. This study will select PV and three energy storage measures, including thermal energy storage, fixed battery and electric vehicle (EV) battery, to form various flexibility strategies and to compare their impacts on life cycle carbon emission based on a prefabricated high-rise residential project in Shenzhen, China, which is comprised of three 32-story buildings and an underground parking, with approximately total 666,000 square feet (62,000 square meter). In order to evaluate both environmental and economic performances, this study introduces 3 indicators: payback period, carbon reduction ratio and cost-carbon rate. The performance of building implemented with different strategies will be compared against a reference situation that without any flexibility strategies. The quantitative analysis of carbon emission in this case study will support architects and building designers to compare multiple flexible strategies and measures to achieve carbon neutrality of building sector. [ABSTRACT FROM AUTHOR]

Published
2023

4. Effect of physical, environmental, and social factors on prediction of building energy consumption for public buildings based on real-world big data.

Author

Zhang, Yuhang, Zhang, Yi, Yi Zhang, and Zhang, Chengxu

Subjects
*ENERGY consumption of buildings, *SOCIAL prediction, *CENTRAL business districts, *SOCIAL factors, *PUBLIC buildings, *EXTERIOR walls, *OFFICE buildings, *DEMAND forecasting, *BIG data
Abstract

In practical operations, various static and dynamic parameters affect the prediction of building energy. In this study, seven modes were constructed based on the Extra-Trees algorithm to investigate the impacts of 19 factors involving physical, environmental, and social aspects on the specific energy consumption. The analysis is supported by data obtained from 110 public buildings over one year in Shenzhen, China. The results indicate that considering more factors significantly improves the prediction accuracy. Factor importance analysis shows that building type is the most significant factor, followed by ambient temperature, month, structure, floor area, and rent price. The external wall material is the least significant factor, followed by the type of heating system and subdistrict population. At least the top 8 most important factors must be provided to maintain an accuracy above 80%. Furthermore, the sensitivity analysis indicates that the SECs of comprehensive and office buildings demonstrate opposite variation trends to floor area; for rent price and distance from central business district, the SECs of comprehensive building show inverse and positive change trends, respectively, but the SECs of office remain almost unchanged. In addition, buildings with internal insulation can lead to more SEC in such climate. • 19 factors involving physical, environmental and social category are considered. • Seven Extra-Trees prediction models are built to investigate influencing factors. • The importance analysis is conducted to rank all the factors. • Sensitivity analysis is made to find the variation trend under eight scenarios. • Recursive feature elimination is made to quantify the factor impact on prediction. [ABSTRACT FROM AUTHOR]

Published
2022
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