A Critical Review of Wind Power Forecasting Methods—Past, Present and Future

Abstract

The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature.

Development of Wind Power Prediction Models for Pawan Danavi Wind Farm in Sri Lanka

“…where D air , A, and V are the air density (in kg/m 3 ), the swept area of wind turbine (in m 3 ), and the upstream wind speed (in m/s), respectively [25]. e density of the air can be represented as function of d, density of the dry air at atmospheric temperature (at 25°C, d � 1.168 kg/m 3 ), T, the absolute temperature (in kelvin), B, the barometric pressure (in torr; 1 atm � 760 torr) and e, the vapor pressure of moist air (in torr) and given in the following equation [30]:…”

“…e temporal-geographical correlations of wind speed in different geographical conditions were studied in China for both linear and nonlinear situations using the linear Pearson coefficient and the nonlinear Spearman rank coefficient and tail correlation coefficient [24]. e performance of wind power prediction models can be assessed by the statistical measures of normalized error, normalized mean biased error, normalized mean absolute error percentage, mean squared logarithmic error, R 2 , explained variance score, and the median absolute error [25].…”

A Short-Term Wind Power Forecasting Approach using ANFIS

“…There are four forecast horizons when considering the wind prediction problem as tabulated below [7]: The boundaries of these timeframes are however bound to different interpretations by different power system operators based on the applications they intend to utilize them for. Short-term planning could extend from 30 mins to 1 day and medium-term categorized as 1 day to several days ahead.…”

“…This method only relies on physical data and does not need to be trained with any historical data. This method is considered complex and one that requires a lot of computational resources [7] and is best suited for medium and long-term wind power prediction. 3.…”

Multistep Wind Speed Forecasting Based on a Hybrid Model of VMD and Nonlinear Autoregressive Neural Network

“…However, they still did not mention daily wind speed prediction. Subsequently, researchers have proposed different referable methods in short-term wind speed forecasting, and it has been proved that machine learning and deep learning algorithms are effective forecasting approaches [7,8]. Han et al studied the application effect of statistical model and neural network algorithm by comparing three different methods on the 1-hour wind speed forecasting, and the conclusion is that compared to statistical models, the neural network has superiority in the treatment of nonlinear characteristics of wind speed, which verified the effectiveness of the neural network in wind speed forecasting issue.…”