Research Publications Authored by SLIIT Staff

Now showing 1 - 3 of 3

Open Access
Forecasting Electricity Power Generation of Pawan Danavi Wind Farm, Sri Lanka, Using Gene Expression Programming
(Hindawi, 2022-05) Herath, D; Jayasinghe, J.M.J.W; Rathnayake, U
is paper presents the development of a wind power forecasting model based on gene expression programming (GEP) for one of the major wind farms in Sri Lanka, Pawan Danavi. With the ever-increasing demand for renewable power generation, Sri Lanka has started harnessing electricity from wind power. ough the initial establishment cost of wind farms is high, the analyses clearly showcased the economic sustainability of wind power generation in long term. In this context, forecasting the wind power generation at Sri Lankan wind farms is important in many ways. However, limited research has been carried out in Sri Lanka to predict the wind power generation against the changing climate. erefore, to overcome this research gap, a model was developed to forecast wind power generation against two climatic factors, viz. on-site wind speed and ambient temperature. e results showcased the robustness and accuracy of the proposed GEP-based forecasting model (with R2 0.92, index of agreement 0.98, and RMSE 259 kW). Moreover, the results of the study were compared against three dierent forecasting models and found comparable in terms of the model accuracy. e GEP-based model is advantageous over machine learning techniques due to its capability in deriving a mathematical expression. As an acceptable relationship was found between wind power generation and climatic factors, the proposed model facilitates the future projection of wind power generations with forecasted climatic factors. ough the application of GEP in the eld of wind power generation is reported in a few research publications, this is the rst research in which GEP is employed to model the power generation with respect to weather indices. e proposed prediction model is advantageous than machine learning models as the relationship between the wind power and the weather indices can be expressed.
Embargo
A Review of Geothermal Energy for Future Power Generation
(IEEE, 2019-09-26) Kulasekara, H; Seynulabdeen, V
Renewable power generation is rapidly increasing due to the depletion and unfavorable environmental impact of fossil fuels. Geothermal energy is a form of renewable energy that can be effectively used for electric power generation. Besides, geothermal power provides considerable advantages compared to other renewable resources such as solar and wind power. Geothermal energy provides reliable, stable and efficient power compared to the lack of inertia, lack of efficiency and the intermittent nature of solar and wind resources. Moreover, geothermal power plants must be integrated with energy storage devices to improve the stability and flexibility of the power system. Gravity-fed energy storage and flywheel energy storage systems are two reliable technologies that can be integrated with geothermal power for improved stability and flexibility. Furthermore, the disadvantages of geothermal energy such as higher initial cost and geographic dependency can be compensated using recent research and developments in the geothermal technology. These recent developments include enhanced geothermal systems, small-scale geothermal power generation and geothermal power generation using abandoned oil and gas wells. Therefore, geothermal energy has the potential to become a major power generating source in the future.
Open Access
Regression-Based Prediction of Power Generation at Samanalawewa Hydropower Plant in Sri Lanka Using Machine Learning
(Hindawi, 2021-07-31) Ekanayake, P; Wickramasinghe, L; Jayasinghe, J. M; Rathnayake, U. S
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.

Research Publications Authored by SLIIT Staff

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