2020 IEEE Green Energy and Smart Systems Conference (IGESSC)最新文献_第3页

Deep Learning Techniques for Traffic Speed Forecasting with Side Information 基于侧信息的交通速度预测的深度学习技术

2020 IEEE Green Energy and Smart Systems Conference (IGESSC) Pub Date : 2020-11-02 DOI: 10.1109/IGESSC50231.2020.9285132

Parinaz Farajiparvar, Nima Hoseinzadeh, Lee D. Han, A. Hedayatipour

引用次数: 1

Timed Petri Nets for Industry 4.0 Electric Motor Manufacturing 工业4.0电机制造的定时Petri网

2020 IEEE Green Energy and Smart Systems Conference (IGESSC) Pub Date : 2020-11-02 DOI: 10.1109/IGESSC50231.2020.9285006

R. McCann, Mireille Tankoua Sandjong

引用次数: 1

Modeling and Development of a HIL Testbed for DER Dynamics Integration Demonstration 用于DER动力学集成演示的HIL试验台建模与开发

2020 IEEE Green Energy and Smart Systems Conference (IGESSC) Pub Date : 2020-11-02 DOI: 10.1109/IGESSC50231.2020.9284981

M. Arifujjaman, R. Salas, A. Johnson, Austen DLima, J. Araiza, J. Mauzey, J. Castaneda

{"title":"Modeling and Development of a HIL Testbed for DER Dynamics Integration Demonstration","authors":"M. Arifujjaman, R. Salas, A. Johnson, Austen DLima, J. Araiza, J. Mauzey, J. Castaneda","doi":"10.1109/IGESSC50231.2020.9284981","DOIUrl":"https://doi.org/10.1109/IGESSC50231.2020.9284981","url":null,"abstract":"The integration of Distributed Energy Resources (DER) into the existing Southern California Edison (SCE) grid has evolved rapidly to accommodate California’s Green House Gas (GHG) reduction goals. The Photovoltaic (PV) systems remain a dominant choice among other DERs and requires an inverter that historically exhibits non-linear characteristics. This criterion underscores the need for a comprehensive PV-inverter model and a sophisticated test bench for demonstrating the operational dynamics and protection functionalities of the system. Given this, a novel impedance-based mathematical modeling is proposed for the PV and inverter. The development of an advanced Hardware-in-the-Loop (HIL) testbed at SCE’s DER Laboratory has described interfaces commercial Rule 21 and IEEE 1547 compliant inverters with the traditional induction and synchronous generator based generations in Real Time Digital Simulator (RTDS) to replicate the simulated model of a medium voltage distribution circuit. Some preliminary simulation and experimentation results yield tremendous agreement and confirm the validity of the modeling approach. The future simulation and demonstration plans are exposed, which show the value of the model and testbed and this contributes evidence to other utilities to further model and develop a testbed for performance evaluations of DER systems.","PeriodicalId":437709,"journal":{"name":"2020 IEEE Green Energy and Smart Systems Conference (IGESSC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114751978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Advanced Mathematical Modeling of Machine Learning and Artificial Intelligent Addressing Satellite Transponder Distortions 机器学习和人工智能寻址卫星转发器畸变的高级数学建模

2020 IEEE Green Energy and Smart Systems Conference (IGESSC) Pub Date : 2020-11-02 DOI: 10.1109/IGESSC50231.2020.9285157

T. Nguyen

引用次数: 0

A Review on Smart LED Lighting Systems 智能LED照明系统研究综述

2020 IEEE Green Energy and Smart Systems Conference (IGESSC) Pub Date : 2020-11-02 DOI: 10.1109/IGESSC50231.2020.9285004

Héctor F. Chinchero, J. Marcos Alonso, Ortiz T. Hugo

引用次数: 4

State of Energy Prediction in Renewable Energy-driven Mobile Edge Computing using CNN-LSTM Networks 基于CNN-LSTM网络的可再生能源驱动移动边缘计算的能量状态预测

2020 IEEE Green Energy and Smart Systems Conference (IGESSC) Pub Date : 2020-11-02 DOI: 10.1109/IGESSC50231.2020.9285102

Yu-Jen Ku, Sandalika Sapra, S. Baidya, S. Dey

{"title":"State of Energy Prediction in Renewable Energy-driven Mobile Edge Computing using CNN-LSTM Networks","authors":"Yu-Jen Ku, Sandalika Sapra, S. Baidya, S. Dey","doi":"10.1109/IGESSC50231.2020.9285102","DOIUrl":"https://doi.org/10.1109/IGESSC50231.2020.9285102","url":null,"abstract":"Renewable energy (RE) is a promising solution to save grid power in mobile edge computing (MEC) systems and thus reducing the carbon footprints. However, to effectively operate the RE-based MEC system, a method for predicting the state of energy (SoE) in the battery is essential, not only to prevent the battery from over-charging or over-discharging, but also allowing the MEC applications to adjust their loads in advance based on the energy availability. In this work, we consider RE-powered MEC systems at the Road-side Unit (RSU) and focus on predicting its battery's SoE by using machine learning technique. We developed a real-world RE-powered RSU testbed consisting of edge computing devices, small cell base station, and solar as well as wind power generators. By operating RE-powered RSU for serving real-world computation task offloading demands, we collect the corresponding data sequences of battery's SoE and other observable parameters of the MEC systems that impact the SoE. Using a variant of Long Short-term Memory (LSTM) model with additional convolutional layers, we form a CNN-LSTM model which can predict the SoE accurately with very low prediction error. Our results show that CNN-LSTM outperforms other Recurrent Neural Networks (RNN) based models for predicting intra-hour and hour-ahead SoE.","PeriodicalId":437709,"journal":{"name":"2020 IEEE Green Energy and Smart Systems Conference (IGESSC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126856304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Multi-Objective Approach for Optimal Size and Location of DGs in Distribution Systems 配电系统中dg最优尺寸和位置的多目标方法

2020 IEEE Green Energy and Smart Systems Conference (IGESSC) Pub Date : 2020-10-05 DOI: 10.1109/IGESSC50231.2020.9285029

Seyed Mohammad Sajjadi Kalajahi, Sina Baghali, T. Khalili, B. Mohammadi-ivatloo, A. Bidram

引用次数: 0