Estimating the short-term and long-term wind speeds: implementing hybrid models through coupling machine learning and linear time series models

Abstract

Wind speed data are of particular importance in the design and management of wind power projects. In the current study, three types of linear time series models including autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) were employed to estimate short-term (i.e., daily) and long-term (i.e., monthly) wind speeds. The required data were gathered, respectively, from the Tabriz and Zahedan stations in the northwest and southeast of Iran. The MA models outperformed the AR and ARMA on the both daily and monthly scales. Daily and monthly wind speed values, as a function of lagged wind speed data, were then estimated using two machine learning models of random forests (RF) and multivariate adaptive regression splines (MARS). It was found that the RF and MARS provided similar results; however, RF performed slightly better than the MARS. Finally, the stand-alone time series and machine learning models were coupled to improve the accuracy of the wind speed estimation. Accordingly, the hybrid RF-AR, RF-MA, RF-ARMA, MARS-AR, MARS-MA, and MARS-ARMA models were implemented. It was concluded that, the hybrid models outperformed the stand-alone RF and MARS for both short-and long-term wind speed estimations where, the RF-AR and MARS-AR hybrid models provided the best performances. The hybrid models tested in the present study could be effective alternatives to the stand-alone machine learning-based RF and MARS models for the estimation of wind speed time series.

Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features

“…When choosing the options for increasing the power generation, the renewable energy source dominates others [1,2]. Renewable energy forecasting mainly solar and wind energies have gained a lot of attention currently due to its vital impact on taking proper operational and managerial decisions in power systems [3,4]. The permanency, grid reliability, reduction of cost and risk level in the energy market is contingent on the accuracy of wind energy prediction [5].…”

Short-Term Prediction of the Wind Speed Based on a Learning Process Control Algorithm in Isolated Power Systems

“…For instance, the authors in [31] proposed the hybrid neural network (NN) model for short-term wind speed forecasting based on a time-series algorithm with the consideration of the multi-learner ensemble and adaptive error correction. The hybrid model that includes three types of linear time series models such as autoregressive, moving average, and autoregressive moving average has been used for both short and long-timescale prediction of wind speeds [32]. Authors in [33] proposed an adaptive neural fuzzy inference system algorithm for a wind speed prediction at 30 s and 60 s based on its historical data of wind speed and direction.…”