Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/892
Title: Regression-Based Prediction of Power Generation at Samanalawewa Hydropower Plant in Sri Lanka Using Machine Learning
Authors: Ekanayake, P
Wickramasinghe, L
Jayasinghe, J. M
Rathnayake, U. S
Keywords: Regression
Based Prediction
Power Generation
Samanalawewa Hydropower Plant
Sri Lanka
Machine Learning
Issue Date: 31-Jul-2021
Publisher: Hindawi
Series/Report no.: Mathematical Problems in Engineering;Vol 2021
Abstract: 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.
URI: http://localhost:80/handle/123456789/892
ISSN: 1024-123X
Appears in Collections:Department of Civil Engineering-Scopes
Research Papers - Department of Civil Engineering
Research Papers - Open Access Research
Research Papers - SLIIT Staff Publications

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