Wind Power Prediction Analysis Based on MI-CEEMDAN-RF-LGBM
In view of the fact that the traditional wind power prediction accuracy can not meet the actual needs of the field,a wind power prediction model was proposed based on the fusion of mutual information(MI),complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),random forest(RF)and light gradi-ent boosting machine(LGBM).MI was used to select a series of turbine parameters such as wind direction,wind speed and temperature,and select the parameter variable related to strong wind power.The original wind power sequence was decomposed into several modal components by using CEEMDAN.In order to prevent the redun-dancy of the data generated by excessive modeling input,RF was used for secondary feature selection to select the feature variables extracted from the features,and further screen out the feature variables that have a high correla-tion with the original wind power sequence.LGBM,Extreme Learning Machine(ELM)and Deep Belief Network(DBN)were adopted to establish wind direction prediction models,and select the wind power prediction models with higher modeling accuracy.The validity of the model were verified by using 53747 sets of wind power,wind direction and wind speed of a wind farm in Guilin with an interval of 10min.
wind power predictionmutual informationlight gradient boosting rnachine
NETL
NSTL
万方数据
