# A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs
###### Abstract
forecasting (WPF) is one of the most efficient ways to reduce the uncertainty on power system operations. Traditional single-value predictions, also called deterministic forecasting, cannot provide uncertainty information. By contrast, probabilistic forecasting is able to provide the set of potential values within which the observation is supposed to lie with a certain probability [4]. There are a variety of probabilistic wind power forecasting methods; they are divided into three main groups [5]: predicted error approach, direct approach, and approaches based on the NWP data. Traditional predicted error approaches were mainly divided into two techniques: parameter-based approach and bootstrap. Regarding the parameter-based approach, several studies described the forecasting uncertainty with suitable probability density functions (PDF) that are generated by historical forecasting errors. For example, Gaussian distribution or beta distribution is the most popular assumption for a PDF. The bootstrap approaches, such as the standard bootstrap, pair bootstrap, and moving block bootstrap, estimate the distribution of forecasting errors by resampling the original data, and an artificial intelligent model can be used to generate forecasting results. For the direct approach, the lower and upper bounds for prediction intervals are trained and constructed by artificial intelligent (AI) technologies. However, the AI model itself is not able to provide Predicted Intervals (PIs). To solve that problem, many developed optimizations were developed. For instance, the lower upper bound estimation (LUBE)-based neural network with a modified bat algorithm (MBA) [6] was used to maximize PI coverage probability (PICP) and minimize PI normalized average width (PINAW). Similarly, quantum-behaved particle swarm optimization (QPSO) was also utilized to optimize PIs [7]. In terms of NWP, spot NWP models consider the physical parameters of weather variables and are widely developed by meteorologists. Most commercial wind power forecasting methods utilized single-value NWP wind forecasts as input data [8; 9]. Ensemble NWPs have been developed widely to provide an assessment of weather uncertainty by varying initial weather parameters in a lower resolution [10]. Moreover, reference [11] proved that the combination of deterministic and probabilistic models enhances wind power forecasts. In this work, NWP ensembles include 20 wind speed members, which would reduce the training speed of the model. Consequently, an autoencoders model was developed for dimensional reduction, which reduces the original NWP ensembles wind speeds and accelerates the model training. Data preprocessing is one of the most important parts for wind power forecasting to extract the features [12; 13], data modification [14], or dimension reduction [15]. The Weather Research and Forecasting (WRF) model is a next-generation mesoscale numerical weather prediction system. It includes deterministic WRF (WRFD), radar WRF (RWRF), and a WRF ensemble prediction system (WEPS). For short-term predictions, the inputs of the forecasting model can comprise historical wind power and different NWP results, including spot NWPs (WRFD and RWRF) and ensemble NWP (WEPS). Therefore, data preprocessing can be applied to wind power, WRFD, RWRF, and WEPS. Furthermore, there are numerous methods to increase the forecasting performance based on NWP adjustments [12; 13]. This paper considers the short-term wind power forecasting, which is important for the intraday trading and the management of the transmission systems. Since the used NWP WEPS wind speeds are generated every three hours, thus, this paper considers the three-hour-ahead short-term wind power forecasting. Additionally, this work develops a hybrid model for probabilistic wind power forecasting. It considers different NWP data, dimensional reduction for ensemble NWPs, and XGBoost. The forecasting module, i.e., prediction interval, in this work uses lower upper bound estimation based on Long Short-Term Memory (LSTM) [16; 17; 18].