Long sequence time-series forecasting with deep learning: A survey
The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …
Pyramid: Enabling hierarchical neural networks with edge computing
Machine learning (ML) is powering a rapidly-increasing number of web applications. As a
crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model …
crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model …
[HTML][HTML] Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey
Electric vehicles are growing in popularity as a form of transportation, but are still underused
for several reasons, such as their relatively low range and the high costs associated with …
for several reasons, such as their relatively low range and the high costs associated with …
Modelling the energy consumption of electric vehicles under uncertain and small data conditions
This study models the energy consumption of electric vehicles (EVs) under uncertain and
small data conditions by combining the machine learning method and the idea of controlled …
small data conditions by combining the machine learning method and the idea of controlled …
Cdgnet: A cross-time dynamic graph-based deep learning model for traffic forecasting
Traffic forecasting is important in intelligent transportation systems of webs and beneficial to
traffic safety, yet is very challenging because of the complex and dynamic spatio-temporal …
traffic safety, yet is very challenging because of the complex and dynamic spatio-temporal …
A preference-aware meta-optimization framework for personalized vehicle energy consumption estimation
Vehicle Energy Consumption (VEC) estimation aims to predict the total energy required for a
given trip before it starts, which is of great importance to trip planning and transportation …
given trip before it starts, which is of great importance to trip planning and transportation …
[HTML][HTML] A system for electric vehicle's energy-aware routing in a transportation network through real-time prediction of energy consumption
S Modi, J Bhattacharya - Complex & Intelligent Systems, 2022 - Springer
To tackle the problem of range anxiety of a driver of an electric vehicle (EV), it is necessary
to accurately estimate the power/energy consumption of EVs in real time, so that drivers can …
to accurately estimate the power/energy consumption of EVs in real time, so that drivers can …
A Bayesian optimized machine learning approach for accurate state of charge estimation of lithium ion batteries used for electric vehicle application
V Selvaraj, I Vairavasundaram - Journal of Energy Storage, 2024 - Elsevier
In recent times, battery technology used in electric vehicles has drawn numerous
researchers' attention. Monitoring the battery condition, particularly the State of charge, is …
researchers' attention. Monitoring the battery condition, particularly the State of charge, is …
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …
sustainable development by harnessing the power of cross-domain data fusion from diverse …
A Bayesian Optimized Deep Learning Approach for Accurate State of Charge Estimation of Lithium Ion Batteries Used for Electric Vehicle Application
S Vedhanayaki, V Indragandhi - IEEE Access, 2024 - ieeexplore.ieee.org
Battery technology used in Electric Vehicles has recently drawn numerous researchers'
attention. Monitoring of battery condition, especially the state of charge, is necessary to …
attention. Monitoring of battery condition, especially the state of charge, is necessary to …