Relationships between Hydropower Generation and Rainfall-Gauged and Ungauged Catchments from Sri Lanka

Perera, Anushka; Rathnayake, Upaka

doi:10.1155/2020/9650251

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…erefore, it could be analytically proved with evidence that out of the four prediction models developed in this study, the GPR has shown excellent performance and even outperformed all the models cited here. In particular, in the previous study conducted on the Samanalawewa hydropower generation, only the ANN was applied, and the performance was evaluated only in terms of the correlation coefficient and the MSE [6]. All the ANN-based prediction models were found Mathematical Problems in Engineering less accurate than the GPR-based model presented in this paper.…”

Section: Quarterly Models Developed Based On Four Meteorological Factors Tablementioning

confidence: 82%

“…However, Beheshti et al [5] expressed reservations on the uncertainties in predicting reservoir variables and hydropower under climate scenarios and suggested further studies, taking the variability in water allocation for irrigation into account. In addition, the complex nonlinear relationship between the rainfall and minihydropower generation in gauged and ungauged catchments of Sri Lanka has been studied recently using ANN, which showed a good correlation between them at the gauged catchments compared to ungauged catchments [6]. Based on the correlation values between the observed and predicted energies, Abdulkadir et al [7] justified the use of neural network approaches in modelling the hydropower generation as a function of reservoir variables at two reservoirs along the River Niger in Nigeria.…”

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: Quarterly Models Developed Based On Four Meteorological Factors Tablementioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

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

Self Cite

View full text Add to dashboard Cite

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Smart hydropower management: utilizing machine learning and deep learning method to enhance dam’s energy generation efficiency

Sahin,

Ozbay Karakus

2024

Neural Comput & Applic

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Renewable energy sources and hydroelectric power generation in large parts of the electricity market are crucial as environmental pollution worsens . Utilizing meteorological data from the region, where the Hirfanlı Dam is located, this study employs machine learning (ML) and introduces a novel hybrid Genetic Grey Wolf Optimizer (GGW0)-based Convolutional Neural Network/Recurrent Neural Network (CNN/RNN) regression technique to predict hydroelectric power production (PP). In the first section of the study, various ML techniques SVR (Support Vector Regression), ELM (Extreme Learning Machine), RFR (Random Forest Regression), ANN (Artificial Neural Networks) and WKNNR (Weighted K-Nearest Neighbor) are presented with the Principal Component Analysis (PCA) method and the minimum–maximum method in the normalization of the features. A novel GGWO and CNN/RNN model)-Long Short-Term Memory (LSTM) regression technique is introduced in the second section. GGWO is used to select features, while the proposed CNN/RNN-LSTM model is employed for feature extraction and prediction of PP. The study demonstrates that the ELM algorithm in Method I outperforms other ML models, achieving a correlation coefficient (r) of 0.977 and the mean absolute error (MAE) of 0.4 with the best feature subset. Additionally, the proposed CNN/RNN hybrid model in Method II yields even better results, with r and MAE values of 0.9802 and 0.314, respectively. The research contributes to the field of renewable energy prediction, and the results can aid in efficient decision making for electricity generation and resource management.

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Impact of climatic factors on the prediction of hydroelectric power generation: a deep CNN-SVR approach

Özbay Karakuş

2023

Geocarto International

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Relationships between Hydropower Generation and Rainfall-Gauged and Ungauged Catchments from Sri Lanka

Cited by 3 publications

References 41 publications

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

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

Smart hydropower management: utilizing machine learning and deep learning method to enhance dam’s energy generation efficiency

Impact of climatic factors on the prediction of hydroelectric power generation: a deep CNN-SVR approach

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