Short-Time Wind Speed Forecast Using Artificial Learning-Based Algorithms

• Published in: Computational intelligence and neuroscience Vol. 2020; no. 2020; pp. 1 - 15
• Main Authors: Al-Dahidi, Sameer, Al-Hindawi, Qays, Alsheikh, Ahmad, Ibrahim, Mariam, ElMoaqet, Hisham
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Alternative energy sources; Analysis; Artificial neural networks; Back propagation; Cable television broadcasting industry; Case studies; Computer applications; Data collection; Decomposition; Forecasting; Forecasting - methods; Forecasting techniques; Genetic algorithms; Harvesting; Industry forecasts; Kalman filters; Learning theory; Long short-term memory; Machine Learning; Mathematical models; Neural networks; Optimization; Power plants; Power sources; Solar energy; Support vector machines; Time series; Wavelet transforms; Wind; Wind farms; Wind power; Wind speed; United States
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2020/8439719

The need for an efficient power source for operating the modern industry has been rapidly increasing in the past years. Therefore, the latest renewable power sources are difficult to be predicted. The generated power is highly dependent on fluctuated factors (such as wind bearing, pressure, wind speed, and humidity of surrounding atmosphere). Thus, accurate forecasting methods are of paramount importance to be developed and employed in practice. In this paper, a case study of a wind harvesting farm is investigated in terms of wind speed collected data. For data like the wind speed that are hard to be predicted, a well built and tested forecasting algorithm must be provided. To accomplish this goal, four neural network-based algorithms: artificial neural network (ANN), convolutional neural network (CNN), long short-term memory (LSTM), and a hybrid model convolutional LSTM (ConvLSTM) that combines LSTM with CNN, and one support vector machine (SVM) model are investigated, evaluated, and compared using different statistical and time indicators to assure that the final model meets the goal that is built for. Results show that even though SVM delivered the most accurate predictions, ConvLSTM was chosen due to its less computational efforts as well as high prediction accuracy.