Abstract
As the most efficient renewable energy source for generating electricity in a modern electricity network, wind power has the potential to realize sustainable energy supply. However, owing to its random and intermittent instincts, a high permeability of wind power into a power network demands accurate and effective wind energy prediction models. This study proposes a multi-stage intelligent algorithm for wind electric power prediction, which combines the Beveridge–Nelson (B-N) decomposition approach, the Least Square Support Vector Machine (LSSVM), and a newly proposed intelligent optimization approach called the Grasshopper Optimization Algorithm (GOA). For data preprocessing, the B-N decomposition approach was employed to disintegrate the hourly wind electric power data into a deterministic trend, a cyclic term, and a random component. Then, the LSSVM optimized by the GOA (denoted GOA-LSSVM) was applied to forecast the future 168 h of the deterministic trend, the cyclic term, and the stochastic component, respectively. Finally, the future hourly wind electric power values can be obtained by multiplying the forecasted values of these three trends. Through comparing the forecasting performance of this proposed method with the LSSVM, the LSSVM optimized by the Fruit-fly Optimization Algorithm (FOA-LSSVM), and the LSSVM optimized by Particle Swarm Optimization (PSO-LSSVM), it is verified that the established multi-stage approach is superior to other models and can increase the precision of wind electric power prediction effectively.
Highlights
With the continuous emergence of global warming, smog weather, and other environmental problems, the development of the conventional thermal power generation mode, which is unsustainable and has large pollutant emissions, has been limited by the renewable energy power generation mode due to its beneficial characteristics
Cyclic component, and random term are in logarithmic form, we need to transform them into natural numbers to apply the Grasshopper Optimization Algorithm (GOA)-Least Square Support Vector Machine (LSSVM) model
For the normalized-Root Mean Square Error (RMSE) values, the average values of GOA-LSSVM and Particle Swarm Optimization (PSO)-LSSVM are both less than 10%, which demonstrate that both of these two models have excellent performance in wind electric power prediction, but the values on Day 1, Day 6, and Day 7 of PSO-LSSVM are all larger than 10%, which are much greater than GOA-LSSVM
Summary
With the continuous emergence of global warming, smog weather, and other environmental problems, the development of the conventional thermal power generation mode, which is unsustainable and has large pollutant emissions, has been limited by the renewable energy power generation mode due to its beneficial characteristics. The primary devotion of this research is to put forward a multi-stage intelligent algorithm for wind electric power forecasting that combines the Beveridge–Nelson (B-N) decomposition approach, the Least Square Support Vector Machine (LSSVM) model, and a newly proposed intelligent optimization algorithm called the Grasshopper Optimization Algorithm (GOA). In the data preprocessing stage, the B-N decomposition approach is utilized to disintegrate the wind electric energy data into a deterministic trend, a periodic term, and a stochastic component. In the forecasting stage, the future data of the three components are computed by the LSSVM, of which the parameters ‘c’ and the Radial Basis Function (RBF) kernel width ‘σ’ are optimally selected by the newly proposed optimization algorithm GOA. Forecasting results illustrate that the proposed multi-stage intelligent algorithm can effectively enhance the precision of wind electric power prediction.
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