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

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.

A CMIP6 multi-model based analysis of potential climate change effects on watershed runoff using SWAT model: A case study of kunhar river basin, Pakistan

“…Moreover, the study offers valuable insights, particularly regarding the near future, that can aid policymakers in the water sector when making decisions related to Sustainable Development Goals, specifically Clean Water & Sanitation (SDG 6), Climate Action (SDG 13) and SDG15 (Life on Land) [ 70 ]. Moreover, the establishment of a new run-of-the-river hydropower plants in the region has the potential to create employment opportunities for the local community [ [71] , [72] , [73] ]. This initiative can contribute to achieving the indicator SDG 1.1, which aims to eradicate extreme poverty for all people everywhere, falling under the broader goal of SDG 1 (No Poverty).…”

One-day ahead forecasting of energy production from run-of-river hydroelectric power plants with a deep learning approach

“…In our study, four statistical error criteria comprising mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (R) are used for assessment of the goodness of a model to estimate an observed output variable. Their calculation methods are as follows [51][52][53]:…”

Influence of Indian Ocean warming on rainfall of Sri Lanka

“…The major crop cultivation seasons in SL thus relies on seasonal rainfall, where the primary cultivation season of Maha (October–March) begins with the onset of OND rainfall, whearas the secondary cultivation season of Yala (April–September) begins with the onset of AMJ rainfall (Burt & Weerasinghe, 2014; Zubair, 2002). In addition, hydropower, which is one of the major sources contributing to the national power supply in SL (Dilini et al, 2014; Ekanayake et al, 2021), also depends on the amount of seasonal rainfall. However, several studies have revealed the potential impacts of extreme rainfall on agriculture and the economy of SL (Withanachchi et al, 2014).…”