Abstract
The actual power output of a hydropower hardly matches the design output, particularly in developing countries. Therefore, this paper presents the performance evaluation of the Kainji hydroelectric power in Nigeria using artificial neural networks and multiple linear regression. Hydrological and power output data that includes reservoir inflow, water surface elevation, turbine discharge and power generation for the power plant were obtained for a period from 2006 to 2019. To predict the power generation, 70% of the dataset is used for model training and the remainder for model validation and testing. Of all the artificial neural network models considered, the model with 3-15-1 architecture was selected as optimum for the power output prediction. Furthermore, to evaluate the performance of the two techniques, the dataset (2016–2019) was fed into the developed models and comparisons were made using the root mean square error and coefficient of determination. Upon evaluation, the performance of the two techniques were found to be satisfactory. However, the multiple linear regression technique provides the most satisfactory as it gives root mean square error of 19.45% and coefficient of determination of 0.91 while artificial neural network had 39.31% and 0.64, respectively, for the power generation prediction. Through the models, the power output was predicted for the 2021–2025. From the results, the maximum power output of 476 MW and 391 MW were predicted for 2021 and 2025, respectively, against a design capacity of 760 MW. Therefore, maintenance of the turbine system is desirable to increase the output of the power plant.
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Acknowledgements
The authors wish to thank the Mainstream Energy Solution Limited (MESL) for the provisions of Kainji Hydro Power Plant hydrology and power output data from 2006 to 2020.
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Ozigis, I.I., Adeyemi, R.A., Ondachi, P.A. et al. Performance evaluation of Kainji hydro-electric power plant using artificial neural networks and multiple linear regression. Int J Energ Water Res 6, 231–241 (2022). https://doi.org/10.1007/s42108-021-00135-3
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DOI: https://doi.org/10.1007/s42108-021-00135-3