Wind Speed Forecasting using Regression, Time Series and Neural Network Models: A Case Study of Suva

Arzu, Arieni and Ahmed, Mohammed R. and Khan, Mohammad G.M. (2020) Wind Speed Forecasting using Regression, Time Series and Neural Network Models: A Case Study of Suva. [Conference Proceedings]

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Abstract

There has been an increase in the capitalization of renewable energy resources to provide greenhouse gas emission-free sources of electricity in order to lessen the effect on climate change. This increase is due to trepidations about climate change, an increase in the energy demand and unpredictability of the prices and supply of fossil fuels. Wind energy is one of the world’s fastest-growing sources of energy. Though abundant, its abundance does not compensate for its stochastic behavior. Forecasting is therefore necessary to increase efficiency and reduce uncertainty. In this paper, the wind speed data are modelled and forecasted using three forecasting techniques: Multiple Linear Regression (MLR), Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN). Four variables: daily wind speed, pressure, relative humidity and temperature were used to develop the wind speed forecast from these models. The performance of the models was evaluated using four measures: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). Results show that the superior model to forecast wind speed is the Multiple Linear Regression. The empirical results reveal that the proposed model using Multiple Linear Regression is more efficient and accurate in forecasting wind speed in comparison to time series models.

Item Type:	Conference Proceedings
Additional Information:	https://doi.org/10.14264/ccee311
Subjects:	Q Science > QA Mathematics T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	School of Information Technology, Engineering, Mathematics and Physics (STEMP)
Depositing User:	Fulori Nainoca - Waqairagata
Date Deposited:	24 Dec 2020 02:17
Last Modified:	24 Dec 2020 02:17
URI:	https://repository.usp.ac.fj/id/eprint/12511

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