Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations
In recent years, there has been a growing emphasis on the efficient utilization of natural resources across various facets of life. One such area of focus is transportation, particularly electric mobility in conjunction with the deployment of renewable energy sources. To fully realize this objective...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/5/1073 |
| _version_ | 1797264614375817216 |
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| author | Pavol Belany Peter Hrabovsky Zuzana Florkova |
| author_facet | Pavol Belany Peter Hrabovsky Zuzana Florkova |
| author_sort | Pavol Belany |
| collection | DOAJ |
| description | In recent years, there has been a growing emphasis on the efficient utilization of natural resources across various facets of life. One such area of focus is transportation, particularly electric mobility in conjunction with the deployment of renewable energy sources. To fully realize this objective, it is crucial to quantify the probability of achieving the desired state—production exceeding consumption. This article deals with the computation of the probability that the energy required to charge an electric vehicle will originate from a renewable source at a specific time and for a predetermined charging duration. The base of the model lies in artificial neural networks, which serve as an ancillary tool for the actual probability assessment. Neural networks are used to forecast the values of energy production and consumption. Following the processing of these data, the probability of energy availability for a given day and month is determined. A total of seven scenarios are calculated, representing individual days of the week. These findings can help users in their decision-making process regarding when and for how long to connect their electric vehicle to a charging station to receive assured clean energy from a local photovoltaic source. |
| first_indexed | 2024-04-25T00:31:42Z |
| format | Article |
| id | doaj.art-ece805ceca3442008766a6cb72644d78 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-25T00:31:42Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ece805ceca3442008766a6cb72644d782024-03-12T16:43:13ZengMDPI AGEnergies1996-10732024-02-01175107310.3390/en17051073Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging StationsPavol Belany0Peter Hrabovsky1Zuzana Florkova2Research Centre, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, SlovakiaResearch Centre, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, SlovakiaResearch Centre, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, SlovakiaIn recent years, there has been a growing emphasis on the efficient utilization of natural resources across various facets of life. One such area of focus is transportation, particularly electric mobility in conjunction with the deployment of renewable energy sources. To fully realize this objective, it is crucial to quantify the probability of achieving the desired state—production exceeding consumption. This article deals with the computation of the probability that the energy required to charge an electric vehicle will originate from a renewable source at a specific time and for a predetermined charging duration. The base of the model lies in artificial neural networks, which serve as an ancillary tool for the actual probability assessment. Neural networks are used to forecast the values of energy production and consumption. Following the processing of these data, the probability of energy availability for a given day and month is determined. A total of seven scenarios are calculated, representing individual days of the week. These findings can help users in their decision-making process regarding when and for how long to connect their electric vehicle to a charging station to receive assured clean energy from a local photovoltaic source.https://www.mdpi.com/1996-1073/17/5/1073electric vehicle chargingphotovoltaic power plantneural network modelscharging station consumptionprobability trendsuser behavior analysis |
| spellingShingle | Pavol Belany Peter Hrabovsky Zuzana Florkova Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations Energies electric vehicle charging photovoltaic power plant neural network models charging station consumption probability trends user behavior analysis |
| title | Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations |
| title_full | Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations |
| title_fullStr | Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations |
| title_full_unstemmed | Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations |
| title_short | Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations |
| title_sort | probability calculation for utilization of photovoltaic energy in electric vehicle charging stations |
| topic | electric vehicle charging photovoltaic power plant neural network models charging station consumption probability trends user behavior analysis |
| url | https://www.mdpi.com/1996-1073/17/5/1073 |
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