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Solar power becomes one of the most promising renewable energy resources inrecent years. However, the weather is continuously changing, and this causes adiscontinuity of energy generation. PV Power forecasting is a suitable solutionto handle sudden disjointedness on energy generation by providing fastdispatching to grid electricity. These methods present a key insight intomatchmaking grid electricity and photovoltaic plants. Bootstrap aggregationEnsemble method(Bagging) is classified as one of the most useful machinelearning models which are applicable to supervised learning regression tasks.Following this regard, this paper proposes a state-of-art method based onbagging and this method works perfectly for PV power forecasting. The latterhad powerful capabilities of tracking the behavior of stochastic problems withgood accuracy with the aid of feature importance information. This approachcomes to optimize bias/variance using feature weighting vector. Thus, thispaper is devoted to present various feature importance techniques forPhotovoltaic forecasting parameters. This technique consists of improving theaforementioned ensemble model via contributing the knowledge expertise obtainedfrom features analysis to be directly transformed into the Ensemble model. Theproposed model is tested on PV power prediction. Therefore, the benchmarkedtechnique shows an improvement in accuracy in terms of RMSE to 5%.

PV Power Forecasting Using Weighted Features for Enhanced Ensemble Method