Short term prediction of wind speed based on long-short term memory networks

Abstract

Power utilities, developers, and investors are pushing towards larger penetrations of wind and solar energy-based power generation in their existing energy mix. This study, specifically, looks towards wind power deployment in Saudi Arabia. For profitable development of wind power, accurate knowledge of wind speed both in spatial and time domains is critical. The wind speed is the most fluctuating and intermittent parameter in nature compared to all the meteorological variables. This uncertain nature of wind speed makes wind power more difficult to predict ahead of time. Wind speed is dependent on meteorological factors such as pressure, temperature, and relative humidity and can be predicted using these meteorological parameters. The forecasting of wind speed is critical for grid management, cost of energy, and quality power supply. This study proposes a short-term, multi-dimensional prediction of wind speed based on Long-Short Term Memory Networks (LSTM). Five models are developed by training the networks with measured hourly mean wind speed values from1980 to 2019 including exogenous inputs (temperature and pressure). The study found that LSTM is a powerful tool for a short-term prediction of wind speed. However, the accuracy of LSTM may be compromised with the inclusion of exogenous features in the training sets and the duration of prediction ahead.

Research on Improving Piano Performance Evaluation Method in Piano Assisted Online Education

“…The purpose of this is to make the error not all attributed to the current neuron in the propagation process, but some of it directly through the "gate" structure, only in this way can the error be well directed to the next layer to avoid the phenomenon of disappearing gradient. This also leads to better convergence [18]. )…”

Roughness sub-layer wind speed model for tropical wooded areas

“…At this sampling frequency the memory became full every 4 days so each tower must be visited in person to obtain the data and create a database document. Considering the erratic nature of the whether parameters, seasonal variability must be addressed (Salman et al, 2021). Therefore this work is performed using year-round data, the minimal recommended by Mohandes et al (2021), from November 2018 to October 2019.…”

“…The aim of this study is to analyze possible expressions for wind speed within the roughness sub-layer, under z * in Figure 1, using experimental data of wind speed at different heights and processing these data with techniques that have proved to be effective in the development of models and correlations, as is symbolic regression (Richmond-Navarro et al, 2017, 2018; Žegklitz and Pošík, 2019). It is proposed that wind speed is not only a function of height, but of other atmospheric variables such as temperature, pressure and relative humidity and can be predicted using these meteorological parameters (Salman et al, 2021). Therefore, the proposal is to describe the conditions under which small scale wind turbines—frequently installed between 0 and 15 m above ground level (AGL) usually operate (Tummala et al, 2016).…”

A Hybrid Model of Variational Mode Decomposition and Long Short-Term Memory for Next-Hour Wind Speed Forecasting in a Hot Desert Climate

“…Other ML model comparisons include studying the effect of including weather variables besides WS and wind direction (WD) on prediction performance, such as [59], in which kernel ridge regression (RR) outperformed SVR and artificial neural network models. Another work [60] studied the effect that using three exogenous variables had on LSTM model performance and found that the best performance is achieved with previous values of WS and T measured at 10 and 2 m. Although all mentioned works targeted the hot desert climate, none of them proposed or compared hybrid models. In addition, no work studied the effect that using exogenous variables on multiple DL models for such a climate.…”