An artificial intelligence model estimating the typical energy usage for larger
In constructing customized machine learning models for fuel consumption, this research recommends a data summary strategy based on distance rather than the conventional time period. Using this technique, an accurate estimate neural network algorithm for median energy usage in heavy trucks is produce...
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| creator | Akarapu, Mahesh Chiranjeevi, Battu Sunitha, Gadipe Ruchinandan, Masani Sheshikala, M. Reddy, Rajasri |
| description | In constructing customized machine learning models for fuel consumption, this research recommends a data summary strategy based on distance rather than the conventional time period. Using this technique, an accurate estimate neural network algorithm for median energy usage in heavy trucks is produced using 7factorsextended velocity of the vehicle ofway slope. For reduce fuel usage across the board, the suggested methodology may be quickly established and implemented for each individual trucks. The predictors from the systems are combined for distance travelled across preset window widths. The results of the evaluation of different window widths show that for paths including both motorway & residential phase shift portions, a 1 km window is capable of estimating energy usage with such a 0.99 coefficient of determination and average utter and total maximum average percentage difference below four percentage. |
| doi_str_mv | 10.1063/5.0195777 |
| format | Conference Proceeding |
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The results of the evaluation of different window widths show that for paths including both motorway & residential phase shift portions, a 1 km window is capable of estimating energy usage with such a 0.99 coefficient of determination and average utter and total maximum average percentage difference below four percentage.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0195777</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Algorithms ; Artificial intelligence ; Energy consumption ; Fuel consumption ; Heavy duty trucks ; Machine learning ; Neural networks</subject><ispartof>AIP conference proceedings, 2024, Vol.2971 (1)</ispartof><rights>Author(s)</rights><rights>2024 Author(s). 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Rajasri</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An artificial intelligence model estimating the typical energy usage for larger</atitle><btitle>AIP conference proceedings</btitle><date>2024-06-05</date><risdate>2024</risdate><volume>2971</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>In constructing customized machine learning models for fuel consumption, this research recommends a data summary strategy based on distance rather than the conventional time period. Using this technique, an accurate estimate neural network algorithm for median energy usage in heavy trucks is produced using 7factorsextended velocity of the vehicle ofway slope. For reduce fuel usage across the board, the suggested methodology may be quickly established and implemented for each individual trucks. The predictors from the systems are combined for distance travelled across preset window widths. The results of the evaluation of different window widths show that for paths including both motorway & residential phase shift portions, a 1 km window is capable of estimating energy usage with such a 0.99 coefficient of determination and average utter and total maximum average percentage difference below four percentage.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0195777</doi><tpages>12</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2024, Vol.2971 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_scitation_primary_10_1063_5_0195777 |
| source | AIP Journals Complete |
| subjects | Algorithms Artificial intelligence Energy consumption Fuel consumption Heavy duty trucks Machine learning Neural networks |
| title | An artificial intelligence model estimating the typical energy usage for larger |
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