Abstract
This paper presents the development of wind power prediction models for a wind farm in Sri Lanka using an artificial neural network (ANN), multiple linear regression (MLR), and power regression (PR) techniques. Power generation data over five years since 2015 were used as the dependent variable in modeling, while the corresponding wind speed and ambient temperature values were used as independent variables. Variation of these three variables over time was analyzed to identify monthly, seasonal, and annual patterns. The monthly patterns are coherent with the seasonal monsoon winds exhibiting little annual variation, in the absence of extreme meteorological changes during the period of 2015–2020. The correlation within each pair of variables was also examined by applying statistical techniques, which are presented in terms of Pearson’s and Spearman’s correlation coefficients. The impact of unit increase (or decrease) in the wind speed and ambient temperature around their mean values on the output power was also quantified. Finally, the accuracy of each model was evaluated by means of the correlation coefficient, root mean squared error (RMSE), bias, and the Nash number. All the models demonstrated acceptable accuracy with correlation coefficient and Nash number closer to 1, very low RMSE, and bias closer to 0. Although the ANN-based model is the most accurate due to advanced features in machine learning, it does not express the generated power output in terms of the independent variables. In contrast, the regression-based statistical models of MLR and PR are advantageous, providing an insight into modeling the power generated by the other wind farms in the same region, which are influenced by similar climate conditions.
Highlights
According to the Sustainable Development Goals adopted by the United Nations in 2015 and the Paris Agreement on Climate Change, the countries are bound to “ensure access to affordable, reliable, sustainable and modern energy for all” by the year 2030
Due to the importance attached to the wind speed among all numerical weather prediction data, the objective of this research was focused on studying the variation of wind power generated by an onshore wind farm with the wind speed and temperature, based on five years of data since 2015. is is essential as the country needs an initiation of research related to wind power. erefore, this paper presents a novel research study based on the wind farm Pawan Danavi to forecast its wind power generation based on the available and most important climatic factors
This study provides an insight into the other existing wind farms in Sri Lanka as well as to the proposed wind power farms, to understand the future wind power generation
Summary
According to the Sustainable Development Goals adopted by the United Nations in 2015 and the Paris Agreement on Climate Change, the countries are bound to “ensure access to affordable, reliable, sustainable and modern energy for all” by the year 2030. In this connection, it is estimated that about two-thirds of global energy demand needs to be fulfilled by renewable energies by the year 2050 in order to contain the rise of global temperature under 2oC [1]. Using wind-powered electricity in developing countries is becoming more popular due to their capacity to bear the maintenance cost and the downward
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