Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms
Date
2022-04-02
Authors
Salah, Saeed
Alsamamra, Husain
Shoqeir, Jawad
Journal Title
Journal ISSN
Volume Title
Publisher
Energies
Abstract
Wind energy is one of the fastest growing sources of energy worldwide. This is clear from
the high volume of wind power applications that have been increased in recent years. However,
the uncertain nature of wind speed induces several challenges towards the development of efficient
applications that require a deep analysis of wind speed data and an accurate wind energy potential at a
site. Therefore, wind speed forecasting plays a crucial rule in reducing this uncertainty and improving
application efficiency. In this paper, we experimented with several forecasting models coming from
both machine-learning and deep-learning paradigms to predict wind speed in a metrological wind
station located in East Jerusalem, Palestine. The wind speed data were obtained, modelled, and
forecasted using six machine-learning techniques, namely Multiple Linear Regression (MLR), lasso
regression, ridge regression, Support Vector Regression (SVR), random forest, and deep Artificial
Neural Network (ANN). Five variables were considered to develop the wind speed prediction models:
timestamp, hourly wind speed, pressure, temperature, and direction. The performance of the models
was evaluated using four statistical error measures: Root Mean Square Error (RMSE), Mean Absolute
Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R
2 ). The experimental results demonstrated that the random forest followed by the LSMT-RNN outperformed
the other techniques in terms of wind speed prediction accuracy for the study site.
Description
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Citation
Salah, S.; Alsamamra, H.R.; Shoqeir, J.H. Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine Learning Algorithms. Energies 2022, 15, 2602. https://doi.org/10.3390/ en15072602