A Combined Forecasting System Based on Modified Multi-Objective Optimization for Short-Term Wind Speed and Wind Power Forecasting

Qingguo Zhou,Qingquan Lv,Gaofeng Zhang

doi:10.3390/app11209383

Abstract

Wind speed and wind power are two important indexes for wind farms. Accurate wind speed and power forecasting can help to improve wind farm management and increase the contribution of wind power to the grid. However, nonlinear and non-stationary wind speed and wind power can influence the forecasting performance of different models. To improve forecasting accuracy and overcome the influence of the original time series on the model, a forecasting system that can effectively forecast wind speed and wind power based on a data pre-processing strategy, a modified multi-objective optimization algorithm, a multiple single forecasting model, and a combined model is developed in this study. A data pre-processing strategy was implemented to determine the wind speed and wind power time series trends and to reduce interference from noise. Multiple artificial neural network forecasting models were used to forecast wind speed and wind power and construct a combined model. To obtain accurate and stable forecasting results, the multi-objective optimization algorithm was employed to optimize the weight of the combined model. As a case study, the developed forecasting system was used to forecast the wind speed and wind power over 10 min from four different sites. The point forecasting and interval forecasting results revealed that the developed forecasting system exceeds all other models with respect to forecasting precision and stability. Thus, the developed system is extremely useful for enhancing forecasting precision and is a reasonable and valid tool for use in intelligent grid programming.

Highlights

With the rise of globalization, renewable and alternative energy sources, which address security issues associated with conventional energy sources, are increasingly being favored to provide power for a wide range of social and economic activities
The original wind speed and wind power time series used for forecastProcedure 2: Prediction of Hybrid Models ing were acquired in 2018 from four observation sites in Shandong province in China
Statistical descriptions for the datasets obtained for the two sites

Summary

Introduction

With the rise of globalization, renewable and alternative energy sources, which address security issues associated with conventional energy sources, are increasingly being favored to provide power for a wide range of social and economic activities. A promising technology utilized in many renewable energy systems, has attracted an increasing amount of attention in recent decades due to the drive to meet the rapidly growing electricity demand across the globe without emitting environmental pollutants, such as CO2. (278324MW) of the world’s total onshore wind power capacity, and accounted for 56%. The accurate forecasting of wind speed—the determining factor in wind energy electricity generation—has increasingly become a focus of public conversation, especially on the shortterm horizon. The capacity of onshore wind power sets has been rapidly increasing, which requires high reliability and maintainability in wind turbines with poor natural conditions. Various methods have been developed to analyze wind turbine rotor blades

Methods

Results

Discussion

Conclusion