FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386983463> ?p ?o ?g. }

• W4386983463 endingPage "121884" @default.
• W4386983463 startingPage "121884" @default.
• W4386983463 abstract "As greener mobility becomes the norm with the advent of electric vehicles (EVs), a natural question arises: how big of a change are we seeing in terms of the stochastic energy demands imposed by EVs? There have been considerable difficulties in analyzing the adoption of all types of EV infrastructure due to the lack of publicly available individual EV user data across various service providers, such as distribution network operators, EV aggregators, EV users, utilities, regulators, and academics. In this study, we introduce the JETPANN (Joint EV energy consumption and charging duration Training Prediction using Artificial Neural Networks), a novel jointly trainable artificial deep neural network framework for predicting stochastic EV user behavior by predicting their stay/charging duration and energy consumption simultaneously. We used a large-scale dataset of individual EV users’ charging transactions collected at a multi-site UCLA campus, the Los Angeles Department of Water and Power (LADWP), the Port of LA, the City of Santa Monica, and the City of Pasadena over a five-year period. This includes more than 50 EVSEs (Electric Vehicle Supply Equipment) and 216 EV charging points. Over 50,000 real-time transactions were recorded in our database with 341 distinct EV users. In this research, essential attributes of each charging session were identified, extracted from our database, and then fed into our JETPANN network. Using a jointly trained framework, our AI-enabled network predicts the stay/charging duration and the energy consumption of all the recorded EV users with an accuracy of above 99 percent. The proposed technique was tested and validated using all the collected historical charging data from individual EV users via realizing mean-absolute errors of training loss versus validation loss. Utilizing hyperparameter tuning and semi-grid search, the JETPANN with semi-optimized hyperparameters was achieved with the lowest mean-absolute errors of 0.927 and 0.068 in predicting stay/charging durations and energy consumptions, respectively, in a jointly trainable framework. This study demonstrates the potential of the JETPANN framework in the prediction of EV users’ behavior by using large-scale real-world data collected from a diverse pool of EV users, including faculty and students, occasional and frequent users, and early and late adopters." @default.
• W4386983463 created "2023-09-24" @default.
• W4386983463 creator A5014375778 @default.
• W4386983463 creator A5066924235 @default.
• W4386983463 creator A5078852705 @default.
• W4386983463 date "2023-12-01" @default.
• W4386983463 modified "2023-10-15" @default.
• W4386983463 title "Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework" @default.
• W4386983463 cites W1064642083 @default.
• W4386983463 cites W114517082 @default.
• W4386983463 cites W150010693 @default.
• W4386983463 cites W2065180801 @default.
• W4386983463 cites W2079055900 @default.
• W4386983463 cites W2201716765 @default.
• W4386983463 cites W2290359803 @default.
• W4386983463 cites W2312154058 @default.
• W4386983463 cites W2353295680 @default.
• W4386983463 cites W2519666521 @default.
• W4386983463 cites W2620993678 @default.
• W4386983463 cites W2755846305 @default.
• W4386983463 cites W2789027126 @default.
• W4386983463 cites W2789344914 @default.
• W4386983463 cites W2806306609 @default.
• W4386983463 cites W2891165369 @default.
• W4386983463 cites W2899038746 @default.
• W4386983463 cites W2908762496 @default.
• W4386983463 cites W2919115771 @default.
• W4386983463 cites W2928641462 @default.
• W4386983463 cites W2952466597 @default.
• W4386983463 cites W2970532630 @default.
• W4386983463 cites W2974747647 @default.
• W4386983463 cites W2981000588 @default.
• W4386983463 cites W2987333402 @default.
• W4386983463 cites W3032655245 @default.
• W4386983463 cites W3042325985 @default.
• W4386983463 cites W3089449646 @default.
• W4386983463 cites W3125650116 @default.
• W4386983463 cites W3155852894 @default.
• W4386983463 cites W3196178504 @default.
• W4386983463 cites W4205325124 @default.
• W4386983463 cites W4213221519 @default.
• W4386983463 cites W4226031225 @default.
• W4386983463 cites W4226160336 @default.
• W4386983463 cites W4280568394 @default.
• W4386983463 cites W4281740950 @default.
• W4386983463 doi "https://doi.org/10.1016/j.apenergy.2023.121884" @default.
• W4386983463 hasPublicationYear "2023" @default.
• W4386983463 type Work @default.
• W4386983463 citedByCount "0" @default.
• W4386983463 crossrefType "journal-article" @default.
• W4386983463 hasAuthorship W4386983463A5014375778 @default.
• W4386983463 hasAuthorship W4386983463A5066924235 @default.
• W4386983463 hasAuthorship W4386983463A5078852705 @default.
• W4386983463 hasBestOaLocation W43869834631 @default.
• W4386983463 hasConcept C112758219 @default.
• W4386983463 hasConcept C119599485 @default.
• W4386983463 hasConcept C121332964 @default.
• W4386983463 hasConcept C124952713 @default.
• W4386983463 hasConcept C127413603 @default.
• W4386983463 hasConcept C136264566 @default.
• W4386983463 hasConcept C142362112 @default.
• W4386983463 hasConcept C154945302 @default.
• W4386983463 hasConcept C162324750 @default.
• W4386983463 hasConcept C163258240 @default.
• W4386983463 hasConcept C206658404 @default.
• W4386983463 hasConcept C2776422217 @default.
• W4386983463 hasConcept C2780165032 @default.
• W4386983463 hasConcept C2780378061 @default.
• W4386983463 hasConcept C41008148 @default.
• W4386983463 hasConcept C44154836 @default.
• W4386983463 hasConcept C50644808 @default.
• W4386983463 hasConcept C62520636 @default.
• W4386983463 hasConceptScore W4386983463C112758219 @default.
• W4386983463 hasConceptScore W4386983463C119599485 @default.
• W4386983463 hasConceptScore W4386983463C121332964 @default.
• W4386983463 hasConceptScore W4386983463C124952713 @default.
• W4386983463 hasConceptScore W4386983463C127413603 @default.
• W4386983463 hasConceptScore W4386983463C136264566 @default.
• W4386983463 hasConceptScore W4386983463C142362112 @default.
• W4386983463 hasConceptScore W4386983463C154945302 @default.
• W4386983463 hasConceptScore W4386983463C162324750 @default.
• W4386983463 hasConceptScore W4386983463C163258240 @default.
• W4386983463 hasConceptScore W4386983463C206658404 @default.
• W4386983463 hasConceptScore W4386983463C2776422217 @default.
• W4386983463 hasConceptScore W4386983463C2780165032 @default.
• W4386983463 hasConceptScore W4386983463C2780378061 @default.
• W4386983463 hasConceptScore W4386983463C41008148 @default.
• W4386983463 hasConceptScore W4386983463C44154836 @default.
• W4386983463 hasConceptScore W4386983463C50644808 @default.
• W4386983463 hasConceptScore W4386983463C62520636 @default.
• W4386983463 hasLocation W43869834631 @default.
• W4386983463 hasOpenAccess W4386983463 @default.
• W4386983463 hasPrimaryLocation W43869834631 @default.
• W4386983463 hasRelatedWork W2034274070 @default.
• W4386983463 hasRelatedWork W2095359398 @default.
• W4386983463 hasRelatedWork W2272886103 @default.
• W4386983463 hasRelatedWork W2386387936 @default.
• W4386983463 hasRelatedWork W2682288891 @default.

• next