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Farshid Keynia (Associate professor)

صفحه فارسی

Institute of Energy / Department of Energy Management and Optimization
  • Academic Major: Electrical Engineering _ Energy
  • Tel: 09133404112 / 3118
  • Follow Me at:     
  • Email: f.keynia [AT] kgut.ac.ir
  • Resume: CV
 

Biography

Farshid Keynia received the B.S., M.S.C., and Ph.D. degrees in electrical engineering from Shahid Bahonar University, Kerman, and Semnan University, Semnan, Iran, in 1996, 2001 and 2010 respectively. At present, he is a Professor with the Energy Department, KGUT, Kerman, Iran. His research interests include forecast processes and control of power systems, operation of electricity markets, artificial intelligence and data mining and its applications to the problems of power systems



Research Interests

Energy management and optimization, Forecasting, Power Market, GEP, TEP, Reliability.

Journals

  1. Two-layer volt/var/total harmonic distortion control in distribution network based on PVs output and load forecast errors doi: 10.1049/iet-gtd.2016.1440

  2. Lifetime efficiency index model for optimal maintenance of power substation equipment based on cuckoo optimisation algorithm doi: 10.1049/iet-gtd.2016.1719

  3. Generation expansion planning by considering energy-efficiency programs in a competitive environment doi: https://doi.org/10.1016/j.ijepes.2015.11.107

  4. Feeder reconfiguration and capacitor allocation in the presence of non-linear loads using new PPSO algorithm doi: 10.1049/iet-gtd.2016.1719

  5. Scrutiny of multifarious particle swarm optimization for finding the optimal size of a PV/wind/battery hybrid system the optimal size of a PV/wind/battery hybrid system doi: https://doi.org/10.1016/j.renene.2015.02.045

  6. A new short-term load forecast method based on neuro-evolutionary algorithm and chaotic feature selection doi: https://doi.org/10.1016/j.ijepes.2014.05.036

  7. A new cascade NN based method to short-term load forecast in deregulated electricity market doi: https://doi.org/10.1016/j.enconman.2013.03.014

  8. A new feature selection algorithm and composite neural network for electricity price forecasting doi: https://doi.org/10.1016/j.engappai.2011.12.001


Conferences

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Books

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