Modelling the power output from a steam power plant in Nigeria

Adequate supply of electricity at a competitive price is pivotal to sustainable development. More often than not, the generation of electricity which drives modern growth and development is currently powered by limited fossil fuels in many nations. Electricity generation and megawatt demand are also...

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Main Authors: Damilola E. Babatunde, Ambrose N. Anozie, James A. Omoleye, Oluwaseun Oyebode, Olubayo M. Babatunde, Augustine O. Ayeni
Format: Article
Language:English
Published: Elsevier 2020-12-01
Series:Energy Reports
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2352484720315511
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author Damilola E. Babatunde
Ambrose N. Anozie
James A. Omoleye
Oluwaseun Oyebode
Olubayo M. Babatunde
Augustine O. Ayeni
author_facet Damilola E. Babatunde
Ambrose N. Anozie
James A. Omoleye
Oluwaseun Oyebode
Olubayo M. Babatunde
Augustine O. Ayeni
author_sort Damilola E. Babatunde
collection DOAJ
description Adequate supply of electricity at a competitive price is pivotal to sustainable development. More often than not, the generation of electricity which drives modern growth and development is currently powered by limited fossil fuels in many nations. Electricity generation and megawatt demand are also usually fluctuating due to several pertinent factors. In a bid to articulate the impact of inherent variations in process parameters on the performance of steam power plant at different loads, this paper presents an investigation into the efficacy of two validation strategies in predicting the net power output from the plant using GMDH Shell software. Using the combinatorial algorithm, the k-fold cross-validation strategy and the training/testing validation technique were applied to empirical data of a power plant in Nigeria. The performance of the models returned from the two validation strategies was evaluated using maximum negative error, maximum positive error, mean absolute percentage error (MAPE), root mean square percentage error (RMSPE), residual sum, the standard deviation of residuals, coefficient of determination (R2) and correlation. For the number of folds and the training/testing split percentage considered in this study, results show that both models obtained were quite competitive, with the k-fold model having a slight edge over the other model. It is expected that the outcome of the study will be handy in researches for providing knowledge base information on choosing and setting optimum operating conditions at various load demand.
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spelling doaj.art-e084a6ba82ae49879bb50d9e7034b7a42022-12-21T18:53:11ZengElsevierEnergy Reports2352-48472020-12-016822828Modelling the power output from a steam power plant in NigeriaDamilola E. Babatunde0Ambrose N. Anozie1James A. Omoleye2Oluwaseun Oyebode3Olubayo M. Babatunde4Augustine O. Ayeni5Department of Chemical Engineering, Covenant University, Ota, Ogun State, Nigeria; Corresponding author.Department of Chemical Engineering, Obafemi Awolowo University, Ile-Ife, Osun State, NigeriaDepartment of Chemical Engineering, Covenant University, Ota, Ogun State, NigeriaCentre for Research in Environmental, Coastal and Hydrological Engineering (CRECHE), Department of Civil Engineering, University of KwaZulu-Natal, Durban 4014, South Africa; Royal HaskoningDHV, Woodmead, Johannesburg 2191, South AfricaDepartment of Electrical and Electronics Engineering, University of Lagos, Nigeria; AB OLUS and Associates, Lagos, NigeriaDepartment of Chemical Engineering, Covenant University, Ota, Ogun State, NigeriaAdequate supply of electricity at a competitive price is pivotal to sustainable development. More often than not, the generation of electricity which drives modern growth and development is currently powered by limited fossil fuels in many nations. Electricity generation and megawatt demand are also usually fluctuating due to several pertinent factors. In a bid to articulate the impact of inherent variations in process parameters on the performance of steam power plant at different loads, this paper presents an investigation into the efficacy of two validation strategies in predicting the net power output from the plant using GMDH Shell software. Using the combinatorial algorithm, the k-fold cross-validation strategy and the training/testing validation technique were applied to empirical data of a power plant in Nigeria. The performance of the models returned from the two validation strategies was evaluated using maximum negative error, maximum positive error, mean absolute percentage error (MAPE), root mean square percentage error (RMSPE), residual sum, the standard deviation of residuals, coefficient of determination (R2) and correlation. For the number of folds and the training/testing split percentage considered in this study, results show that both models obtained were quite competitive, with the k-fold model having a slight edge over the other model. It is expected that the outcome of the study will be handy in researches for providing knowledge base information on choosing and setting optimum operating conditions at various load demand.http://www.sciencedirect.com/science/article/pii/S2352484720315511Power generationFossil fuelsSustainabilityMegawatt demandsProcess variablesArtificial intelligence
spellingShingle Damilola E. Babatunde
Ambrose N. Anozie
James A. Omoleye
Oluwaseun Oyebode
Olubayo M. Babatunde
Augustine O. Ayeni
Modelling the power output from a steam power plant in Nigeria
Energy Reports
Power generation
Fossil fuels
Sustainability
Megawatt demands
Process variables
Artificial intelligence
title Modelling the power output from a steam power plant in Nigeria
title_full Modelling the power output from a steam power plant in Nigeria
title_fullStr Modelling the power output from a steam power plant in Nigeria
title_full_unstemmed Modelling the power output from a steam power plant in Nigeria
title_short Modelling the power output from a steam power plant in Nigeria
title_sort modelling the power output from a steam power plant in nigeria
topic Power generation
Fossil fuels
Sustainability
Megawatt demands
Process variables
Artificial intelligence
url http://www.sciencedirect.com/science/article/pii/S2352484720315511
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