Random forest regression model based on improved fruit fly optimization algorithm and its application in wind speed forecasting

Abstract

Abstract: To solve the problem that it is difficult to determine the combination of parameters and obtain the precise forecasting results for the wind speed forecasting based on random forest (RF), an improved fruit fly optimization algorithm (IFOA) was used to optimize the parameters of RFR. Exponential function and trigonometric function were introduced into the fruit fly optimization algorithm (FOA) to realize the adaptive update of step size in search, which enhances the algorithm’s ability of global optimization and local exploration. Combining the advantages of RFR with a good tolerance for noise and abnormal values, IFOA was used to optimize the main parameters of RFR, and the optimized model was applied to wind speed forecasting. The experimental results show that the IFOA-RFR combined model has higher prediction accuracy compared with other models, and the feasibility of this method in wind speed prediction is verified.

Key words: fruit fly optimization algorithm, random forest, parameter optimization, wind speed forecasting

CLC Number:

TM614

ZHU Chang-sheng, LI Sui-han. Random forest regression model based on improved fruit fly optimization algorithm and its application in wind speed forecasting[J]. Journal of Lanzhou University of Technology, 2021, 47(4): 83-90.

References

[1] MISHRA S P,DASH P K.Short term wind speed prediction using multiple kernel pseudo inverse neural network [J].International Journal of Automation and Computing,2018,15(1):66-83.
[2] 田中大,李树江,王艳红,等.短期风速时间序列混沌特性分析及预测 [J].物理学报,2015,64(3):236-247.
[3] LIU D,NIUD X,WANG H,et al.Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm [J].Renewable Energy,2014,62:592-597.
[4] 张妍,韩璞,王东风,等.基于变分模态分解和LSSVM的风电场短期风速预测 [J].太阳能学报,2018,39(1):194-202.
[5] QIN Q,FENG Y W,LI F.Structural reliability analysis using enhanced cuckoo search algorithm and artificial neural network [J].Journal of Systems Engineering and Electronics,2018,29(6):1317-1326.
[6] 唐舟进,任峰,彭涛,等.基于迭代误差补偿的混沌时间序列最小二乘支持向量机预测算法 [J].物理学报,2014,63(5):78-87.
[7] AMANI T,JORDI M,ALI K,et al.Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments [J].Chinese Physics B,2014,23(4):414-425.
[8] BREIMAN L.Random forests [J].Machine Learning,2001,45(1):5-32.
[9] 李国,江晓东.基于提升回归树与随机森林的风电功率集成预测方法 [J].电力系统及其自动化学报,2018,30(11):70-74.
[10] 方馨蕊,温兆飞,陈吉龙,等.随机森林回归模型的悬浮泥沙浓度遥感估算 [J].遥感学报,2019,23(4):756-772.
[11] SUN Z X,SUN H X,ZHANG J X.Multistep wind speed and wind power prediction based on a predictive deep belief network and an optimized random forest [J].Mathematical Problems in Engineering,2018,4:1-15.
[12] 赵东,臧雪柏,赵宏伟.基于果蝇优化的随机森林预测方法 [J].吉林大学学报(工学版),2017,47(2):609-614.
[13] 李东辉,尹海燕,郑博文.基于MFOA-GRNN模型的年电力负荷预测 [J].电网技术,2018,42(2):585-590.
[14] LOH W X.Classification and regression trees [J].Wiley Interdisciplinary Reviews Data Mining & Knowledge Discovery,2011,1(1):14-23.
[15] BREIMAN L.Bagging predictors [J].Machine Learning,1996,24(2):123-140.
[16] PAN W T.A new fruit fly optimization algorithm:taking the financial distress model as an example [J].Knowledge-Based Systems,2012,26:69-74.
[17] WU L,LIU Q,TIAN X,et al.A new improved fruit fly optimization algorithm IAFOA and its application to solve engineering optimization problems [J].Knowledge-Based Systems,2018,144:153-173.
[18] GENUER R,POGGI J M,TULEAU-MALOT C.Variable selection using random forests [J].Pattern Recognition Letters,2010,31(14):2225-2236.
[19] PROBST P,WRIGHT M,BOULESTEIX A L.Hyperparameters and tuning strategies for random forest [J].Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery,2018,9(3):1-18.
[20] RODRIGUEZ-GALIANO V,GHIMIRE B,ROGAN J,et al.An assessment of the effectiveness of a random forest classifier for land-cover classification [J].ISPRS Journal of Photogrammetry and Remote Sensing,2012,67:93-104.