Examining influential factors on the energy consumption of electric and diesel buses: A data-driven analysis of large-scale public transit network in Beijing

Energy ◽  
2021 ◽  
Vol 216 ◽  
pp. 119196
Author(s):  
Xiaolei Ma ◽  
Ran Miao ◽  
Xinkai Wu ◽  
Xianglong Liu
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Public Transit ◽  
Influential Factors ◽  
Data Driven ◽  
Transit Network ◽  
Diesel Buses

scholarly journals A Data-driven Approach for Sustainable Building Retrofit—A Case Study of Different Climate Zones in China

2020 ◽  
Vol 12 (11) ◽  
pp. 4726 ◽  
Author(s):  
Qiong He ◽  
S. Thomas Ng ◽  
Md. Uzzal Hossain ◽  
Godfried L. Augenbroe
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Residential Buildings ◽  
Energy Performance ◽  
Data Driven ◽  
Total Energy Consumption ◽  
Climatic Zones ◽  
Climate Zones ◽  
Cold Zone ◽  
High Rise

This study presents a data-driven retrofitting approach by systematically analyzing the energy performance of existing high-rise residential buildings using a normative calculation logic-based simulation method. To demonstrate the practicality of the approach, typical existing buildings in five climate zones of China are analyzed based on the local building characteristics and climatic conditions. The results show that the total energy consumption is 544 kWh/m2/year in the severe cold zone, which is slightly higher than that in the cold zone (519 kWh/m2/year), but double that in the hot summer and cold winter zone, three times higher than that in the warm zone, and five times above that in the temperate zone. The dominant energy needs in different climatic zones are distinctive. The identified potentially suitable retrofitting measures are important in reducing large-scale energy consumption and can be used in supporting sustainable retrofit decisions for existing high-rise residential buildings in different climatic zones.

scholarly journals A Low-Carbon-Based Bilevel Optimization Model for Public Transit Network

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Xu Sun ◽  
Hua-pu Lu ◽  
Wen-jun Chu
Keyword(s):  
Energy Consumption ◽  
Public Transit ◽  
Bilevel Optimization ◽  
Environmental Cost ◽  
Pollutant Emission ◽  
Low Carbon ◽  
Transit Network ◽  
Traffic Efficiency ◽  
De Algorithm ◽  
Time Operation

To satisfy the demand of low-carbon transportation, this paper studies the optimization of public transit network based on the concept of low carbon. Taking travel time, operation cost, energy consumption, pollutant emission, and traffic efficiency as the optimization objectives, a bilevel model is proposed in order to maximize the benefits of both travelers and operators and minimize the environmental cost. Then the model is solved with the differential evolution (DE) algorithm and applied to a real network of Baoji city. The results show that the model can not only ensure the benefits of travelers and operators, but can also reduce pollutant emission and energy consumption caused by the operations of buses, which reflects the concept of low carbon.

scholarly journals Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4334
Author(s):  
Yujie Xu ◽  
Vivian Loftness ◽  
Edson Severnini
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Energy Savings ◽  
Detection System ◽  
Effective Means ◽  
The United States ◽  
Data Driven ◽  
Commercial Building ◽  
Carbon Footprints ◽  
Site Energy

Buildings account for 40% of the energy consumption and 31% of the CO2 emissions in the United States. Energy retrofits of existing buildings provide an effective means to reduce building consumption and carbon footprints. A key step in retrofit planning is to predict the effect of various potential retrofits on energy consumption. Decision-makers currently look to simulation-based tools for detailed assessments of a large range of retrofit options. However, simulations often require detailed building characteristic inputs, high expertise, and extensive computational power, presenting challenges for considering portfolios of buildings or evaluating large-scale policy proposals. Data-driven methods offer an alternative approach to retrofit analysis that could be more easily applied to portfolio-wide retrofit plans. However, current applications focus heavily on evaluating past retrofits, providing little decision support for future retrofits. This paper uses data from a portfolio of 550 federal buildings and demonstrates a data-driven approach to generalizing the heterogeneous treatment effect of past retrofits to predict future savings potential for assisting retrofit planning. The main findings include the following: (1) There is high variation in the predicted savings across retrofitted buildings, (2) GSALink, a dashboard tool and fault detection system, commissioning, and HVAC investments had the highest average savings among the six actions analyzed; and (3) by targeting high savers, there is a 110–300 billion Btu improvement potential for the portfolio in site energy savings (the equivalent of 12–32% of the portfolio-total site energy consumption).

Interference mitigation in point to point wireless sensor networks using LSP protocol and time division multiplexing approach

2020 ◽  
Vol 39 (4) ◽  
pp. 5449-5458
Author(s):  
A. Arokiaraj Jovith ◽  
S.V. Kasmir Raja ◽  
A. Razia Sulthana
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Control Parameter ◽  
Interference Mitigation ◽  
Wireless Sensor ◽  
Point To Point ◽  
A Minor ◽  
Two Stages ◽  
Time Division ◽  
And Control

Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission.

Research on Hyperpaths in the Urban Rail Transit Network

2014 ◽  
Vol 488-489 ◽  
pp. 1439-1443
Author(s):  
Jin Hai Li ◽  
Jian Feng Liu
Keyword(s):  
Large Scale ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Network ◽  
Travel Costs ◽  
Urban Rail ◽  
Distinctive Structure ◽  
Feasible Path ◽  
Definition Of ◽  
Urban Rail Transit Network

Hyperpaths enumeration is one of the basic procedures in many traffic planning issues. As a result of its distinctive structure, hyperpaths in Urban Rail Transit Network (URTN) are different from those in road network. Typically, one may never visit a station more than once and would never transfer from one line to another that has been visited in a loopless URTN, meaning that stations a hyperpath traversed cannot be repeated, neither do lines in loopless networks. This paper studies the relationships between feasible path and the shortest path in terms of travel costs. In this paper, a new definition of hyperpath in URTN is proposed and a new algorithm based on the breadth first searching (BFS) method is presented to enumerate the hyperpaths. The algorithm can safely avoid hyperpath omission and can even be applied in networks containing loops as well. The influence of parameters on hyperpaths is studied by experimentally finding hyperpaths in the subway network in Beijing. A group of suggested parameter pairs are then given. Finally, a numerical experiment is used to illustrate the validity of the proposed algorithm. The results imply the significance of the convergence of the BFS algorithm which can be used to search hyperpaths in large scale URTN even with loop.

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

scholarly journals Automated Data-Driven Generation of Personalized Pedagogical Interventions in Intelligent Tutoring Systems

2021 ◽  
Author(s):  
Ekaterina Kochmar ◽  
Dung Do Vu ◽  
Robert Belfer ◽  
Varun Gupta ◽  
Iulian Vlad Serban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Student Performance ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Large Scale ◽  
Intelligent Tutoring ◽  
Performance Outcomes ◽  
Data Driven ◽  
Personalized Feedback ◽  
Tutoring Systems

AbstractIntelligent tutoring systems (ITS) have been shown to be highly effective at promoting learning as compared to other computer-based instructional approaches. However, many ITS rely heavily on expert design and hand-crafted rules. This makes them difficult to build and transfer across domains and limits their potential efficacy. In this paper, we investigate how feedback in a large-scale ITS can be automatically generated in a data-driven way, and more specifically how personalization of feedback can lead to improvements in student performance outcomes. First, in this paper we propose a machine learning approach to generate personalized feedback in an automated way, which takes individual needs of students into account, while alleviating the need of expert intervention and design of hand-crafted rules. We leverage state-of-the-art machine learning and natural language processing techniques to provide students with personalized feedback using hints and Wikipedia-based explanations. Second, we demonstrate that personalized feedback leads to improved success rates at solving exercises in practice: our personalized feedback model is used in , a large-scale dialogue-based ITS with around 20,000 students launched in 2019. We present the results of experiments with students and show that the automated, data-driven, personalized feedback leads to a significant overall improvement of 22.95% in student performance outcomes and substantial improvements in the subjective evaluation of the feedback.

scholarly journals Perceived Trip Time Reliability and Its Cost in a Rail Transit Network

2021 ◽  
Vol 13 (13) ◽  
pp. 7504
Author(s):  
Jie Liu ◽  
Paul Schonfeld ◽  
Jinqu Chen ◽  
Yong Yin ◽  
Qiyuan Peng
Keyword(s):  
Regression Models ◽  
Influential Factors ◽  
Rail Transit ◽  
Linear Regression Models ◽  
Transit Network ◽  
Time Index ◽  
Trip Time ◽  
Buffer Time ◽  
Transfer Times

Time reliability in a Rail Transit Network (RTN) is usually measured according to clock-based trip time, while the travel conditions such as travel comfort and convenience cannot be reflected by clock-based trip time. Here, the crowding level of trains, seat availability, and transfer times are considered to compute passengers’ Perceived Trip Time (PTT). Compared with the average PTT, the extra PTT needed for arriving reliably, which equals the 95th percentile PTT minus the average PTT, is converted into the monetary cost for estimating Perceived Time Reliability Cost (PTRC). The ratio of extra PTT needed for arriving reliably to the average PTT referring to the buffer time index is proposed to measure Perceived Time Reliability (PTR). To overcome the difficulty of obtaining passengers’ PTT who travel among rail transit modes, a Monte Carlo simulation is applied to generated passengers’ PTT for computing PTR and PTRC. A case study of Chengdu’s RTN shows that the proposed metrics and method measure the PTR and PTRC in an RTN effectively. PTTR, PTRC, and influential factors have significant linear relations among them, and the obtained linear regression models among them can guide passengers to travel reliably.

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