###### Abstract
[7] proposed a novel hybrid forecasting system consisting of an effective denoising technique and a fuzzy time series method optimized by a multi-objective differential evolution algorithm to improve the forecasting accuracy and stability. Significantly, as an effective and promising tool, deep learning is expert in extracting inherent non-linear and high-order invariant features in data and has been applied in various fields [12]. For example, [PERSON] et al. [13] developed a novel short-term wind forecasting method based on the concepts of learning ability and transfer learning of integrated technology in a deep neural network. It is undeniable that statistical methods play an important role in short-term wind forecasting. However, the accuracy of statistical models is essentially dependent on historical data, hence they are powerless in the event of dramatic changes in weather conditions. In general, the previous short-term wind forecasting methods focus on point prediction and are vulnerable to the historical data. Although CFD-based physical model prediction can obtain a large amount of wind field information, it is time consuming and cannot achieve real-time prediction. Therefore, it is of practical value to develop a new approach based on real-time meteorological data to obtain wind field information quickly and accurately, which needs to solve the problems of the statistical model's susceptibility to weather conditions and the time-consuming nature of physical modeling. In order to solve the above problems, the feature extraction method can be applied to the CFD wind field calculation results with reference to the numerical reconstruction method in the field of aerodynamics [14; 15; 16], which creatively connects wind reconstruction to wind forecasting. The combination of wind reconstruction and wind forecasting is based on the following assumptions: Because wind farms are generally much smaller than wind fields, the speed distribution in wind farms is related to the wind speed beyond a certain distance (e.g., at the boundary of the wind field), which implies that there is a time buffer from the boundary to the wind farm. Therefore, in this period, the velocity of each point in the wind farm can be reconstructed rapidly using the measured values at the boundary, and the time spent in calculating the reconstruction represents the time scale in the ultra-short-term prediction. The main feature extraction methods used in aerodynamics are Principal Component Analysis (PCA) [17] and Proper Orthogonal Decomposition (POD) [18]. However, both feature extraction methods are applied to 2D flow field reconstruction, and the research on 3D flow field reconstruction is relatively scarce. Moreover, in wind field reconstruction, there are abundant studies on velocity field reconstruction, but few on pressure field reconstruction. In addition, the placement of limited sensors is the key factor affecting the reconstruction results, hence optimizing sensor placement to improve reconstruction results may be critically enabling. In recent years, several studies of optimal sensor placement have been undertaken [19; 20; 21]. For example, [PERSON] [16] presented an optimization algorithm based on a minimum condition number theory to determine the sensor placement. Significantly, most optimization methods are generally based on a brute-force combinatorial search with greedy algorithms, which is characterized by its ability to determine the optimal position effectively, but its disadvantage is also very prominent, i.e., it is very time-consuming, and the combinatorial search does not scale well to a large 3D wind field.