Journal ArticleDOI
An improved vehicle to the grid method with battery longevity management in a microgrid application
Qingqing Yang,Qingqing Yang,Jianwei Li,Jianwei Li,Jianwei Li,Wanke Cao,Shuangqi Li,Jie Lin,Da Huo,Hongwen He +9 more
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TLDR
An improved vehicle-to-grid (V2G) scheduling approach for the frequency control with the advantage of protecting the batteries hence saving the battery lifetime during grid connected service is proposed.About:
This article is published in Energy.The article was published on 2020-05-01. It has received 39 citations till now. The article focuses on the topics: Charge cycle & Electric vehicle.read more
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A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.
TL;DR: This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction of lithium-ion batteries, and chooses the high-accuracy deep convolutional neural network — extreme learning machine algorithm to be utilized.
Journal ArticleDOI
Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network
Gong Cheng,Xinzhi Wang,Yurong He +2 more
TL;DR: This work combined the empirical mode decomposition (EMD) method and backpropagation long-short-term memory (B-LSTM) neural network (NN) to develop SOH estimation and RUL prediction models that have high robustness, good accuracy, and applicability.
Journal ArticleDOI
Benefits of electric vehicles integrating into power grid
Wei Wu,Boqiang Lin +1 more
TL;DR: In this article, the authors examined the economic value of electric vehicles integrating into the power grid and established an electricity supply cost model to investigate the cost reduction under three charging operation modes, including random charging, controlled charging, and V2G charging.
Journal ArticleDOI
The role of artificial intelligence in the mass adoption of electric vehicles
TL;DR: The authors in this paper reviewed the recent advances in EVs and related infrastructure, mainly from artificial intelligence (AI), which makes EVs a more attractive consumer option, and analyzed the application of AI in improving EVs, facilitating EV charging stations, and EV integration with the smart grid.
Journal ArticleDOI
Boosting Grid Efficiency and Resiliency by Releasing V2G Potentiality Through a Novel Rolling Prediction-Decision Framework and Deep-LSTM Algorithm
TL;DR: A brand-new rolling prediction-decision framework for V2G scheduling is designed to bridge the gap between optimization and forecasting phases, where the predicted information can be more reasonably and adequately utilized.
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Posted Content
An overview of gradient descent optimization algorithms
TL;DR: This article looks at different variants of gradient descent, summarize challenges, introduce the most common optimization algorithms, review architectures in a parallel and distributed setting, and investigate additional strategies for optimizing gradient descent.
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Introduction to Online Convex Optimization
TL;DR: This monograph portrays optimization as a process, by applying an optimization method that learns as one goes along, learning from experience as more aspects of the problem are observed.
Journal ArticleDOI
Predicting residential energy consumption using CNN-LSTM neural networks
Tae Young Kim,Sung-Bae Cho +1 more
TL;DR: This paper proposes a CNN-LSTM neural network that can extract spatial and temporal features to effectively predict the housing energy consumption and achieves almost perfect prediction performance for electric energy consumption that was previously difficult to predict.
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Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks
TL;DR: A recurrent neural network model to make medium-to-long term predictions of electricity consumption profiles in commercial and residential buildings at one-hour resolution and uses the deep NN to perform imputation on an electricity consumption dataset containing segments of missing values is presented.
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Short-Term Residential Load Forecasting Based on Resident Behaviour Learning
TL;DR: In this article, a long short-term memory-based deep-learning forecasting framework with appliance consumption sequences is proposed to address the volatile problem in residential load forecasting, which can be notably improved by including appliance measurements in the training data.
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