Time - variations of wave energy and forecasting power availability at a site in Fiji using time-series, regression and ANN techniques

Kumar, Avikesh and Bulivou, Gabiriele and Ahmed, Mohammed R. and Khan, Mohammad G.M. (2024) Time - variations of wave energy and forecasting power availability at a site in Fiji using time-series, regression and ANN techniques. Journal of the Royal Society of New Zealand, NA . NA. ISSN 0303-6758

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Abstract

Recently, there has been a shift in the global energy landscape to move to reliable, clean, and eco-friendly renewable energy sources to address global issues such as climate change and greenhouse gas emissions. One such energy source is wave energy; researchers attempt to develop models that can accurately forecast the availability of wave energy as an alternative energy source. In this paper, an Artificial Neural Network (ANN) model along with statistical models such as time series models, and regression models are proposed for forecasting wave energy at a site in Fiji using the wave height and wave period as the independent variables. The performance of the proposed models developed is compared using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the goodness-of-fit (R2) value. The proposed model is then further benchmarked with the naïve model. The empirical results reveal that the proposed ANN model outclassed all the other models and was more efficient and accurate in forecasting wave energy than the regression and time series models. By accurate wave modelling and by incorporating impedance matching, maximum power generation can be achieved.

Item Type:	Journal Article
Subjects:	Q Science > Q Science (General) > Q1-390 Science (General) Q Science > QA Mathematics T Technology > TJ Mechanical engineering and machinery
Divisions:	School of Information Technology, Engineering, Mathematics and Physics (STEMP)
Depositing User:	Gabiriele Bulivou
Date Deposited:	24 Jan 2025 00:35
Last Modified:	24 Jan 2025 00:35
URI:	https://repository.usp.ac.fj/id/eprint/14637

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