scholarly journals Time series behavior modeling with digital twin for Internet of Vehicles

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Tianle Zhang ◽  
Xiangtao Liu ◽  
Zongwei Luo ◽  
Fuqiang Dong ◽  
Yu Jiang
Keyword(s):  
Time Series ◽  
Real World ◽  
Power Distribution ◽  
Large Scale ◽  
Behavior Modeling ◽  
Low Carbon ◽  
Distribution Grid ◽  
Charging Infrastructure ◽  
Traffic Pattern ◽  
Digital Twin

AbstractElectric vehicle (EV) is considered eco-friendly with low carbon emission and maintenance costs. Given the current battery and charging technology, driving experience of EVs relies heavily on the availability and reachability of EV charging infrastructure. As the number of charging piles increases, carefully designed arrangement of resources and efficient utilization of the infrastructure is essential to the future development of EV industry. The mobility and distribution of EVs determine the charging demand and the load of power distribution grid. Then, dynamic traffic pattern of numerous interconnected EVs poses great impact on charging plans and charging infrastructure.In this paper, we introduce the digital twin of a real-world EV by modeling the mobility based on a time series behaviors of EVs to evaluate the charging algorithm and pile arrangement policy. The introduced digital twin EV is a virtually simulated equivalence with same traffic behaviors and charging activities as the EV in real world. The behavior and route choice of EVs is dynamically simulated base on the time-varying driving operations, travel intent, and charging plan in a simulated large-scale charging scenario composed of concurrently moving EVs and correspondingly equipped charging piles. Different EV navigation algorithms and charging algorithms of Internet of Vehicle can be exactly evaluated in the dynamic simulation of the digital twins of the moving EVs and charging infrastructure. Then we analyze the collected data such as energy consumption, charging capacity, charging frequency, and waiting time in queue on both the EV side and the charging pile side to evaluate the charging efficiency. The simulation is used to study the relations between the scheduled charging operation of EVs and the deployment of piles. The proposed model helps evaluate and validate the design of the charging recommendation and the deployment plan regarding to the arrangement and distribution of charging piles.

scholarly journals Modeling the Impact of the Vehicle-to-Grid Services on the Hourly Operation of the Power Distribution Grid

Designs ◽  
2018 ◽  
Vol 2 (4) ◽  
pp. 55 ◽  
Author(s):  
Tawfiq Aljohani ◽  
Osama Mohammed
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Test System ◽  
Distribution Grid ◽  
Dynamic Simulations ◽  
Charging Infrastructure ◽  
Vehicle To Grid ◽  
Available Energy ◽  
Stability Issues ◽  
The Impact

Electric Vehicles (EVs) impact on the grid could be very high. Unless we monitor and control the integration of EVs, the distribution network might experience unexpected high or low load that might exceed the system voltage limits, leading to severe stability issues. On the other hand, the available energy stored in the EVs can be utilized to free the distribution system from some of the congested load at certain times or to allow the grid to charge more EVs at any time of the day, including peak hours. This article presents dynamic simulations of the hour-to-hour operation of the distribution feeder to measure the grid’s reaction to the EV’s charging and discharging process. Four case scenarios were modeled here considering a 24-h distribution system load data on the IEEE 34 bus feeder. The results show the level of charging and discharging that were allowed on this test system, during each hour of the day, before violating the limits of the system. It also estimates the costs of charging throughout the day, utilizing time-of-use rates as well as the number of EVs to be charged on an hourly basis on each bus and provide hints on the best locations on the system to establish the charging infrastructure.

scholarly journals Comparison of Stochastic and Machine Learning Methods for Multi-Step Ahead Forecasting of Hydrological Processes

2017 ◽  
Author(s):  
Georgia A. Papacharalampous ◽  
Hristos Tyralis ◽  
Demetris Koutsoyiannis
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Stochastic Processes ◽  
Real World ◽  
Large Scale ◽  
Simulation Experiment ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Stationary Stochastic Processes ◽  
First Time

We perform an extensive comparison between 11 stochastic to 9 machine learning methods regarding their multi-step ahead forecasting properties by conducting 12 large-scale computational experiments. Each of these experiments uses 2 000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 110 values and the second time using time series of 310 values. Additionally, we conduct 92 real-world case studies using mean monthly time series of streamflow and particularly focus on one of them to reinforce the findings and highlight important facts. We quantify the performance of the methods using 18 metrics. The results indicate that the machine learning methods do not differ dramatically from the stochastic, while none of the methods under comparison is uniformly better or worse than the rest. However, there are methods that are regularly better or worse than others according to specific metrics.

scholarly journals Efficient Load Management for BEV Charging Infrastructure in Multi-Apartment Buildings

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5927
Author(s):  
Jasmine Ramsebner ◽  
Albert Hiesl ◽  
Reinhard Haas
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Load Management ◽  
Model Parameters ◽  
Distribution Grid ◽  
Charging Infrastructure ◽  
Apartment Buildings ◽  
Technical Requirements ◽  
Private Network ◽  
The Impact

Interest in and demand for battery electric vehicles (BEVs) is growing strongly due to the increasing awareness of climate change and specific decarbonization goals. One of the largest challenges remains the provision of large-scale, efficient charging infrastructure in multi-apartment buildings. Successful load management (LM) for BEV charging directly influences the technical requirements and the economic and environmental aspects of charging infrastructure and can prevent costly distribution grid expansion. The main objective of this paper is to evaluate potential LM approaches in multi-apartment buildings to avoid an increase in existing electricity demand peaks with BEV diffusion. Using our model parameters, off-peak charging achieved a 40% reduction in the building’s demand peak at 100% BEV diffusion compared to uncontrolled charging and reduced the correlation between BEV charging and the national share of thermal power generation. The most efficient charging capacity in the private network was achieved at 0.44 kW/BEV. A verification of the model results with the demonstration phase of the “Urcharge” project supports our overall findings. Our results outline the advantages of LM across a large-scale BEV charging network to control the impact on the electricity system along with the diffusion of e-mobility.

scholarly journals Learning towards Abstractive Timeline Summarization

2019 ◽  
Author(s):  
Xiuying Chen ◽  
Zhangming Chan ◽  
Shen Gao ◽  
Meng-Hsuan Yu ◽  
Dongyan Zhao ◽  
...  
Keyword(s):  
Time Series ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Event Memory ◽  
Document Summarization ◽  
Word Level ◽  
Level Information ◽  
Time Position ◽  
Evolution Trajectory

Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods.In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events.Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order.To tackle this challenge, we propose a memory-based timeline summarization model (MTS).Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process.Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events.Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations.

Model Identification and Parameter Tuning of Dynamic Loads in Power Distribution Grid: Digital Twin Approach

2021 ◽  
Author(s):  
Nils Huxoll ◽  
Mohannad Aldebs ◽  
Payam Teimourzadeh Baboli ◽  
Sebastian Lehnhoff ◽  
Davood Babazadeh
Keyword(s):  
Power Distribution ◽  
Model Identification ◽  
Parameter Tuning ◽  
Dynamic Loads ◽  
Distribution Grid ◽  
Digital Twin

1588-P: Therapy Trends in Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1588-P ◽  
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
AMBRISH MITHAL ◽  
SHASHANK JOSHI ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Nationwide Study

2258-PUB: Impact of Cardiovascular (CV) Risk Factors during the Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2258-PUB
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
SHASHANK JOSHI ◽  
AMBRISH MITHAL ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Risk Factors ◽  
Real World ◽  
Large Scale ◽  
Nationwide Study

Phenomenological Modelling

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Real World ◽  
Biological Diversity ◽  
False Positives ◽  
World Systems ◽  
Economic Activities ◽  
Essential Services ◽  
Low Dimension ◽  
Convergent Cross Mapping

Detecting causal interactions among climatic, environmental, and human forces in complex biophysical systems is essential for understanding how these systems function and how public policies can be devised that protect the flow of essential services to biological diversity, agriculture, and other core economic activities. Convergent Cross Mapping (CCM) detects causal networks in real-world systems diagnosed with deterministic, low-dimension, and nonlinear dynamics. If CCM detects correspondence between phase spaces reconstructed from observed time series variables, then the variables are determined to causally interact in the same dynamic system. CCM can give false positives by misconstruing synchronized variables as causally interactive. Extended (delayed) CCM screens for false positives among synchronized variables.

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