evoDLA: A semantic learning system for predictive cost minimization in power generation system


  • Z. O. O. Jagun Department of Computer Engineering Olabisi Onabanjo University, Ogun State, Nigeria.
  • M. B. Olajide Department of Electrical & Electronic Engineering, Olabisi Onabanjo University Ogun State, Nigeria.
  • B. A. Wokoma Department of Electrical Engineering, Rivers State University, Port Harcourt, Nigeria.
  • E. N. Osegi Department of Information Technology, National Open University, Nigeria.


In this paper, we propose evolutionary deviant learning system (evoDLA) for optimal power generating systems. It is a temporally structured learning system for the predictive cost minimization task at the next time step expressed in a semantic way. Cost minimization in power generation is an assignment problem that requires the determination of the best set of operating parameters to enable generators perform optimally. Presented as an alternative to the conventional unit commitment strategies, the predictive system is designed to operate in a semi-supervised manner allowing the dynamic superimposition of linguistic memory units into the mixed-integer power system generation data. A set of representative linguistic biased integers are programmed into an equivalent fuzzy-like integer representation. These are transformed through a series of temporal deviant states and then re-transformed back into true linguistic form. Simulation studies are performed in comparison with the Long Short-Term Memory (LSTM), a proven artificial neural network (ANN) method for sequential learning tasks. The results show competitive performance with the present system and the unique capability of the novel system in inferring the optimal set of output generation parameters.


2023-06-28 — Updated on 2023-06-28

How to Cite

Jagun, Z. O. O. ., Olajide, M. B., Wokoma, B. A., & Osegi, E. N. (2023). evoDLA: A semantic learning system for predictive cost minimization in power generation system. Kathmandu University Journal of Science Engineering and Technology, 17(1). Retrieved from https://journals.ku.edu.np/kuset/article/view/119