Climate Variation and Hydropower Generation in Samanalawewa Hydropower Scheme, Sri Lanka

Dabare, Geeshani; Gunathilake, Miyuru B.; Miguntanna, Nandika; Laksiri, Kamal; Rathnayake, Upaka

doi:10.4038/engineer.v53i3.7384

Cited by 1 publication

(2 citation statements)

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“…ough a couple of studies addressed this problem recently, a comprehensive research on the prediction of power generation based on all related weather indices has not yet been conducted [1].…”

Section: Introductionmentioning

confidence: 99%

“…In a nonparametric statistical analysis of the monthly data over 26 years of the catchment rainfall associated with the Samanalawewa power plant in Sri Lanka, Dabare et al [1] showed a positive correlation between the rainfall and the hydropower generation. While proposing nonlinear analysis for more specific conclusions, this study disavowed concerns on the negative impact of climate change on the rainfall.…”

Section: Introductionmentioning

confidence: 99%

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Regression-Based Prediction of Power Generation at Samanalawewa Hydropower Plant in Sri Lanka Using Machine Learning

Ekanayake

Wickramasinghe

Jayasinghe

et al. 2021

Mathematical Problems in Engineering

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This paper presents the development of models for the prediction of power generation at the Samanalawewa hydropower plant, which is one of the major power stations in Sri Lanka. Four regression-based machine learning and statistical techniques were applied to develop the prediction models. Rainfall data at six locations in the catchment area of the Samanalawewa reservoir from 1993 to 2019 were used as the main input variables. The minimum and maximum temperature and evaporation at the reservoir site were also incorporated. The collinearities between the variables were investigated in terms of Pearson’s and Spearman’s correlation coefficients. It was found that rainfall at one location is less impactful on power generation, while that at other locations are highly correlated with each other. Prediction models based on monthly and quarterly data were developed, and their performance was evaluated in terms of the correlation coefficient (R), mean absolute percentage error (MAPE), ratio of the root mean square error (RMSE) to the standard deviation of measured data (RSR), BIAS, and the Nash number. Of the Gaussian process regression (GPR), support vector regression (SVR), multiple linear regression (MLR), and power regression (PR), the machine learning techniques (GPR and SVR) produced the comparably accurate prediction models. Being the most accurate prediction model, the GPR produced the best correlation coefficient closer to 1 with a very less error. This model could be used in predicting the hydropower generation at the Samanalawewa power station using the rainfall forecast.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%