All Outputs (5)

Multiple decomposition-aided long short-term memory network for enhanced short-term wind power forecasting. (2023)
Journal Article
BALCI, M., DOKUR, E., YUZGEC, U. and ERDOGAN, N. 2024. Multiple decomposition-aided long short-term memory network for enhanced short-term wind power forecasting. IET renewable power generation [online], 18(3), pages 331-347. Available from: https://doi.org/10.1049/rpg2.12919

With the increasing penetration of grid-scale wind energy systems, accurate wind power forecasting is critical to optimizing their integration into the power system, ensuring operational reliability, and enabling efficient system asset utilization. A... Read More about Multiple decomposition-aided long short-term memory network for enhanced short-term wind power forecasting..

Impact of communication system characteristics on electric vehicle grid integration: a large-scale practical assessment of the UK's cellular network for the Internet of Energy. (2023)
Journal Article
ZEINALI, M., ERDOGAN, N., BAYRAM, I.S. and THOMPSON, J.S. 2023. Impact of communication system characteristics on electric vehicle grid integration: a large-scale practical assessment of the UK's cellular network for the Internet of Energy. Electricity [online], 4(4), pages 309-319. Available from: https://doi.org/10.3390/electricity4040018

The ever-increasing number of plug-in electric vehicles (PEVs) requires appropriate electric vehicle grid integration (EVGI) for charging coordination to maintain grid stability and enhance PEV user convenience. As such, the widespread adoption of el... Read More about Impact of communication system characteristics on electric vehicle grid integration: a large-scale practical assessment of the UK's cellular network for the Internet of Energy..

A new rough ordinal priority-based decision support system for purchasing electric vehicles. (2023)
Journal Article
KUCUKSARI, S., PAMUCAR, D., DEVECI, M., ERDOGAN, N. and DELEN, D. 2023. A new rough ordinal priority-based decision support system for purchasing electric vehicles. Information sciences [online], 647, article number 119443. Available from: https://doi.org/10.1016/j.ins.2023.119443

This study proposes a novel multi-criteria decision-making (MCDM) model based on a rough extension of the Ordinal Priority Approach (OPA) to determine the order of importance of users' perspectives on Electric Vehicle (EV) purchases. Unlike conventio... Read More about A new rough ordinal priority-based decision support system for purchasing electric vehicles..

A rough Dombi Bonferroni based approach for public charging station type selection. (2023)
Journal Article
DEVECI, M., ERDOGAN, N., PAMUCAR, D., KUCUKSARI, S. and CALI, U. 2023. A rough Dombi Bonferroni based approach for public charging station type selection. Applied energy [online], 345, article 121258. Available from: https://doi.org/10.1016/j.apenergy.2023.121258

As the transition to electric mobility accelerates, charging infrastructure is rapidly expanding. Publicly accessible chargers, also known as electric vehicle supply equipment (EVSE), are critical not only for further promoting the transition but als... Read More about A rough Dombi Bonferroni based approach for public charging station type selection..

Mutation based improved dragonfly optimization algorithm for a neuro-fuzzy system in short term wind speed forecasting. (2023)
Journal Article
PARMAKSIZ, H., YUZGEC, U., DOKUR, E. and ERDOGAN, N. 2023. Mutation based improved dragonfly optimization algorithm for a neuro-fuzzy system in short term wind speed forecasting. Knowledge-based systems [online], 268, article 110472. Available from: https://doi.org/10.1016/j.knosys.2023.110472

The Dragonfly algorithm (DA) is a heuristic optimization algorithm that is commonly used for complex optimization problems. Despite its widespread application, the abundance of social behaviors in its construct can lead to poor accuracy in solutions... Read More about Mutation based improved dragonfly optimization algorithm for a neuro-fuzzy system in short term wind speed forecasting..