GTFD‐XTNet: A tabular learning‐based ensemble approach for short‐term prediction of photovoltaic power

IET Renewable Power GenerationVolume 16, Issue 12 p. 2682-2693 ORIGINAL RESEARCHOpen Access GTFD-XTNet: A tabular learning-based ensemble approach for short-term prediction of photovoltaic power Qinglun Wang, Qinglun Wang orcid.org/0000-0001-8503-7408 College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, ChinaSearch for more papers by this authorSongjian Chai, Songjian Chai College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, ChinaSearch for more papers by this authorYun Liu, Corresponding Author Yun Liu liuyun19881026@gmail.com School of Electric Power Engineering, South China University of Technology, Guangzhou, China Correspondence Yun Liu, School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China. Email: liuyun19881026@gmail.comSearch for more papers by this authorGuibin Wang, Guibin Wang College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, ChinaSearch for more papers by this author Qinglun Wang, Qinglun Wang orcid.org/0000-0001-8503-7408 College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, ChinaSearch for more papers by this authorSongjian Chai, Songjian Chai College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, ChinaSearch for more papers by this authorYun Liu, Corresponding Author Yun Liu liuyun19881026@gmail.com School of Electric Power Engineering, South China University of Technology, Guangzhou, China Correspondence Yun Liu, School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China. Email: liuyun19881026@gmail.comSearch for more papers by this authorGuibin Wang, Guibin Wang College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, ChinaSearch for more papers by this author First published: 14 July 2022 https://doi.org/10.1049/rpg2.12494AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Greenhouse effect has led to the deterioration of the global climate, so the development of renewable energy is extremely urgent. Photovoltaic (PV) generation has attracted much attention in recent years, but it is limited by the uncertain and intermittent nature, which needs promising PV prediction models. In this study, we propose a novel hybrid deep learning model based on the TabNet, extreme gradient boosting (XGBoost) and linear regression, which is motivated by the ensemble and residual ideas. The models of XGBoost and TabNet are fitted simultaneously based on numeric weather prediction (NWP) information. We adopt the linear regression to combine the results from both TabNet and XGBoost to render the final estimates through ResNet approaches. Besides, the gradient boosting decision tree (GBDT) is incorporated for redundant feature reduction and important feature selection. Meanwhile, the general time features difference (GTFD) is also developed in this work to augment the original feature space and to mine the implicit temporal relationships among features. The proposed model is validated in real-life PV data and the experimental results show that the GTFD-XTNet model outperforms the state-of-the-art forecasting models in the PV field. 1 INTRODUCTION 1.1 Motivation In recent years, many countries have launched renewable energy reform proposals to cope with the climate change issues, as the frequent extreme weather caused by global warming imposes huge impact on our economy and society. As the promising alternative energy, photovoltaic (PV) generation has experienced a dramatic increase in the last decade. According to Snapshot of Global PV Markets 2021 [30], solar PV had another record-breaking year in 2020, with new installations demanding consumption 3.7% of the world. However, the output of PV power is largely affected by the weather conditions, such as solar irradiance, temperature, cloud cover etc. This brings about great challenges for the power grid operation and the energy system management. To solve this arduous task, an effective and accurate PV power forecasting tool is highly needed, which will help the grid operators make economic and reliable energy scheduling decisions, thus guaranteeing the secure operation of entire power system. 1.2 Literature review The state-of-the-art PV forecasting models can be broadly divided into three main categories based on the modelling process [26, 33], the traditional time series forecasting method, machine learning algorithm and deep learning algorithm. Traditionally, PV power forecasting mainly relies on the auto-regressive moving average (ARMA) model and its variants. In literature [9], ARMA is utilized to predict the future hourly PV power scenarios by Bismark Sing and David Pozo, where they summarize a step-by-step methodology to forecast power using hourly ARMA models. The ARMA model is suitable for stationary data analysis, but this method requires static time series data. Yet, Hansen [31] found the ARMA model is only suitable in stationary data analysis, which limits its wide application for unsatanic time series. In [12], Wang et al. utilize support vector machine (SVM) with particle swarm optimization (PSO) to predict PV power based on weather information. They introduce inertial weights to enhance the convergence of the algorithm. This proposed SVM-PSO outperforms other SVMs with different optimization mechanisms. However, the inertia weights of this model are constant and cannot be automatically updated; hence, it is difficult to adapt to changing weather condition and the generalization ability of the prediction model is limited. Parvez et al. in [10] apply multi-layers perceptron (MLP) to predict the 24-h ahead in Italy by using the numerical weather prediction (NWP) dataset global horizontal irradiance (GHI). The model inputs include the daily GHI, ambient temperature and the days of each month. The numerical results show a correlation coefficient of PV measurements and GHI 0.94 and 0.98 for cloudy and sunny days, respectively. The mean absolute error (MAE) and mean bias error (MBE) are 3.21% and 8.54%, respectively. In literature [1], the authors propose the wavelet transformation-deep convolutional neural network (WT-DCNN) for 45-min-ahead PV power forecasting. The model input is the weather information after wavelet transformation. This model has fewer parameters to be optimized and the layer parameters are shared. However, as the number of layers increasing, the number of parameters increases exponentially. In [14], the long short-term memory network with convolutional neural network model (LSTM-CNN) is proposed to perform the spatiotemporal PV power prediction. LSTM-CNN has the merits of exploring the temporal and spatial characteristics of PV power series and its corresponding explanatory variables across multiple stations and look-ahead horizons, thus achieving better model robustness and accuracy. Since the performance of the model is highly influenced by the quality of the used data, data processing methods greatly affect the predictive performance [8]. In article [17], the authors use wavelet analysis, which is based on wavelet transform, to divide a time series into different scales and frequency ranges. Nevertheless, in the process of data filtering, this method may discard information that is intensely correlated with tags. Therefore, it is difficult to retain vital features, which makes the prediction effect poorly. In [4], Fei et al. use the K-means clustering algorithm to classify the historical weather information, which divides the same kind of weather indicators into one category as a feature for forecasting. Although this improves the interpretability of the data, it leads to the loss of calendar information and extracts the correlation of NWP. There are other studies that some people use principal component analysis (PCA) technology to acquire NWP information with fewer features, which can reduce the running time [3]. But it will compress dataset space, and the representation of data is damaged badly. In addition, the dimensionality of the compressed dataset in PCA is subjectively defined by human behaviour, which will result in excessive randomness. In reference [25], Gao et al. propose a CEEMD-WT and CNN model. The CEEMD algorithm is employed to decompose the original historical wind speed values into constitutive series and a residual sequence. Then, the WT algorithm performs secondary denoising for functions to eliminate the influence of noise. Finally, implicit information of NWP information is automatically extracted by the CNN model, and the final prediction results are reconstructed by all former predictions. However, this method abandons the temporal sequence and correlation information in the data, because the CNN model can only extract the spatial information of the sequence data [5]. However, most of these methods are difficult to retain the importance degrees of different features, or to extract the temporal relationship involved in variables. 1.3 Main contributions (1) In order to enhance the features, this paper proposes a generalized time feature difference method (GTFD). It integrates NWP weather variables and time increment information to effectively exploit the temporal correlation information involved in the underlying features. (2) A novel XTNet architecture is developed by combining the TabNet and XGBoost model, which intends to use greater splitting mechanism and fitting performance. In addition, the residual error and linear regression are considered to correct the predicted results of the TabNet and XGBoost, and the weights of them can be found automatically. Meanwhile, this model can automatically select features and find correlation with different variables through the internal GBDT unit. 1.4 Organization The main organization of this paper is as follows: Section 2 introduces the GTFD-XTNet model and its internal components. In Section 3, we carry out dedicated numerical experiments and comparative studies to demonstrate the effectiveness of the proposed GTFD-XTNet model. The article is summarized in the last section. 2 THE MODELLING AND ALGORITHM OF THE PROPOSED GTFD-XTNET 2.1 A general description of the proposed GTFD-XTNet The traditional MLP attempts to forge a direct mapping relationship between weather information and PV power to analyse future changes of predicted targets [13]. However, the temporal correlation and feature enhancement are not considered in the MLP, which fails to explore the relationship between NWP data and temporal information. As a sequential deep learning model, the LSTM model accounts for the temporal correlation by leveraging the gate mechanism, but it lacks interpretability [6, 25]. On the other hand, these methods are ineffective to select the most important features from all concerned variables. In the proposed GTFD-XTNet model, as shown in Figure 1, the GTFD module intends to construct cross statistical features from the model input according to time increments. The main purpose of the XTNet model is to mine the potential internal relations of existing time series features and select the most important features. XTNet is comprised of four basic units: TabNet, XGBoost, GBDT and linear regression. The functionality of each component will be detailed in the following sections. FIGURE 1Open in figure viewerPowerPoint The structure of the proposed GTFD-XTNet. GTFD, general time features difference. 2.2 General time-features difference (GTFD) method Some weather influencing factors have considerable impact on the amount of PV power generation, such as light intensity and atmospheric clouds. These influencing factors show different physical characteristics in different time periods. For example, the solar irradiance can reach the highest level at noon and reach the lowest level at night. Therefore, it is important to consider the influence of temporal information of weather variables. Generally, GTFD attempts to obtain the extra statistical features based on time feature difference. In total, five statistical features are additionally derived in this study, namely feature difference, mean value of the grouped feature difference, standard deviation of the grouped feature difference, maximum and minimum values of the grouped feature difference. Δ x j , t d = x j , t d − x j , t − 1 d \begin{equation}\Delta x_{j,t}^d = x_{j,t}^d - x_{j,t - 1}^d\end{equation} (1) μ j , t = mean ( G i , j | Δ x j , t d ) \begin{equation}{\mu _{j,t}} = {\rm{mean}}({G_{i,j}}|\Delta x_{j,t}^d)\end{equation} (2) σ j , t = std ( G i , j | Δ x j , t d ) \begin{equation}{\sigma _{j,t}} = {\rm{std}}({G_{i,j}}|\Delta x_{j,t}^d)\end{equation} (3) max j , t = max ( G i , j | Δ x j , t d ) \begin{equation}{\max _{j,t}} = \max ({G_{i,j}}|\Delta x_{j,t}^d)\end{equation} (4) min j , t = min ( G i , j | Δ x j , t d ) \begin{equation}{\min _{j,t}} = \min ({G_{i,j}}|\Delta x_{j,t}^d) \end{equation} (5)where x j , t d $x_{j,t}^d$ is the jth feature value at time t in day d, j = 1,2,…, J, J is the total number of features. Δ x j , t d $\Delta x_{j,t}^d$ represents the increment of feature value x j , t d $x_{j,t}^d$ which is calculated by two adjacent time points. Δ x j , t d $\Delta x_{j,t}^d$ is further grouped by setting several bins. In Equation (2), we group these bins of Δ x j , t d $\Delta x_{j,t}^d$ to calculate the average of jth feature value as μ j , t ${\mu _{j,t}}$ . The G i , j ${G_{i,j}}$ represents groups as ith bunch of samples and jth feature. The μ j , t ${\mu _{j,t}}$ can reflect the overall distribution of the influencing factors. In Equation (3), the standard deviation σ j , t ${\sigma _{j,t}}$ is also calculated to analyse the discrete degree of weather changes. In order to get the value range of x j , t d $x_{j,t}^d$ , maximum max j , t ${\max _{j,t}}$ and minimum min j , t ${\min _{j,t}}$ are displayed in Equations (4) and (5) of the proposed method. By getting the standard deviation, maximum and minimum, the abnormal values are replaced to reduce the fluctuation of x j , t d $x_{j,t}^d$ . Based on these various groups, we apply these statistics functions into all features. Therefore, many cross statistics features are constructed by this method. The advantage of GTFD is that it quantifies the variations of NWP and is able to discover the incremental trend. Therefore, these statistical features can reflect distributions and variations of weather conditions. The whole process of GTFD can be summarized as X enhanced = G T F D ( X original ) \begin{equation}{X_{{\rm{enhanced}}}} = GTFD({X_{{\rm{original}}}})\end{equation} (6) X inp = { X original , X enhanced } \begin{equation}{X_{{\rm{inp}}}} = \{ {X_{{\rm{original}}}},{X_{{\rm{enhanced}}}}\} \end{equation} (7)where Xinp, Xoriginal and Xenhanced represent model input features, original NWP features and the enhanced features by GTFD transformation, respectively. 2.3 XTNet 2.3.1 GBDT-based feature selection GBDT combines several base classifiers into a strong one to reduce the data dimension, while retaining the features that highly correlated to the targets, and thus improving the learning efficiency [21]. Firstly, the GBDT model is used to fit the relationship between features and target, the weight coefficients of each feature can thus be obtained [22]. Then, the redundant features are removed and important features are retained. The model uses the efficient strategy of leaf growth to judge whether features are necessary. Distinguishing from the traditional boosting methods, GBDT makes global convergence of algorithm by following the direction of the negative gradient [11], and the overall training process fits a classification and regression tree (CART) regression tree. After completing the training of decision tree, GBDT will sum up the results of all trees. The model parameters are updated by reducing the value of loss function. Therefore, we can build a model where the loss function decreases in the negative gradient direction. The whole learning process of GBDT is described as follows: Let L represent the loss function; x is the value of sample and y is the predicted targets; symbol β is model step size; m represents the number of samples. The steps of GBDT are presented as follows: (1) The initial value is given as F 0 ( x ) = a r g m i n β ∑ i = 1 N L ( y i , β ) \begin{equation}{F_0}(x) = arg\mathop {min}\limits_\beta \sum_{i = 1}^N {L(yi,\beta )} \end{equation} (8) (2) The negative gradient direction is calculated y ∗ = − ∂ L ( y i , F ( x i ) ) ∂ F ( x i ) F ( x ) − F m − 1 ( x ) , i = { 1 , 2 , … , N } \begin{equation}{y^{\rm{*}}}{\rm{ = - }}{\left[ {\frac{{\partial L({y_i},F({x_i}))}}{{\partial F({x_i})}}} \right]_{F(x) - {F_{m - 1}}(x)}}{\rm{,}}i = \{ {\rm{1,2, \ldots }},N\} \end{equation} (9) (3) The meta classifiers are utilized to train model [16]. In the training, parameter α m ${\alpha _m}$ and the h ( x i ; α m ) $h({x_i};{\alpha _m})$ are fitted by using the least-squares approach. a m = a r g m i n α , β ∑ i = 1 N [ y i ∗ − β h ( x i ; a ) ] 2 \begin{equation}{a_m} = arg\mathop {min}\limits_{\alpha ,\beta } \sum_{i = 1}^N {{{[y_i^* - \beta h({x_i};a)]}^2}} \end{equation} (10) (4) The new step size βm is calculated by this way. β m = a r g m i n α , β ∑ i = 1 N L ( y i , F m − 1 ( x ) + β h ( x i ; a ) ) \begin{equation}{\beta _m} = arg\mathop {min}\limits_{\alpha ,\beta } \sum_{i = 1}^N {L({y_i},{F_{m - 1}}(x) + \beta h({x_i};a))} \end{equation} (11) (5) The model is updated as follows: F m ( x ) = F m − 1 ( x ) + β m h ( x i ; a ) \begin{equation}{F_m}(x) = {F_{m - 1}}(x) + {\beta _m}h({x_i};a)\end{equation} (12) Based on the splitting mechanism of GBDT, the extracted features can be expressed by X GBDT = G B D T ( X inp ) \begin{equation}{X_{{\rm{GBDT}}}} = GBDT({X_{{\rm{inp}}}})\end{equation} (13)where the XGBDT represents the features selected by GBDT. 2.4 TabNet TabNet model is a new table learning model that integrates decision trees (DTs) into deep neural network (DNN), which is proposed by Google in 2020. The TabNet model is characterized by the encoder-decoder architecture [28]. Generally, the encoder consists of multiple layers, each layer contains feature transformer, attentive transformer and mask modules, which is illustrated in Figure 2 [29]. Feature transformer processes the filtered features and separates them to prepare for the total decision output and the next decision. On the other hand, by sparse selection of important features, attentive transformer efficiently accelerates model training process without wasting for learning on irrelevant features in each round of decision-making [7, 24]. Important features are then selected by the feature masking module. The feature importance scores are calculated to find the global importance of table features. The details of these crucial components are presented as follows. FIGURE 2Open in figure viewerPowerPoint The structure of the TabNet encoder Encoder Feature transformer: As shown in Figures 3, Feature Transformer is composed of two blocks, namely parametric shared module and non-parametric shared module. Parametric shared module computes feature similarity, and the non-parametric shared module computes the characteristic of features. Both modules are composed of two-layered NN, wherein each layer contains the well-known fully connected layers (FC), batch normalization (BN) and gated linear units (GLU). FIGURE 3Open in figure viewerPowerPoint The inner structure of the feature transformer GLU is a combination of a convolution layer output with linear function, and a convolution layer output with sigmoid non-linear activation function. h t ( X ) = ( X × W + b ) ⊗ δ ( X × V + c ) \begin{equation}{h_t}(X) = (X \times W + b) \otimes \delta (X \times V + c)\end{equation} (14) δ = 1 1 + e − x \begin{equation}\delta = \frac{1}{{1 + {e^{ - x}}}}\end{equation} (15)where X is input; W, V, b and c represent the learned parameters; δ is the sigmoid function. ⊗ is the matrix product. Attentive transformer: As shown in Figure 4, a sequential attention mechanism is presented in the TabNet to extract a subset of meaningful features at each decision. Instance-wise feature selection enables efficient learning as the model capacity is fully used for the most prominent features. The method allows the model to learn more important features, yields more interpretable information via selection masks and makes training efficiently [12]. Before the current decision step, the transcendental results are considered the weight to multiply the features, and the prior knowledge could be updated by instance-wise feature selection. The feature selection method in this part can be regarded as an optimization to minimize the Euclidean distance between input x and data distribution p, and select top several features with the highest probability [20]. s p a r s e m a x ( x ) ≔ arg m i n p ∈ ▵ K − 1 p − x \begin{equation}sparsemax(x)\coloneq \mathop {\arg min}\limits_{p \in \vartriangle K - 1} \left\| {p - x\left\| {} \right.} \right.\end{equation} (16) FIGURE 4Open in figure viewerPowerPoint The structure of the attention transformer Feature masking: The mask mechanism was originally used in the field of natural language processing to inform the model whether certain parts of the data should be remained or ignored. To remain or ignore the prominent features, the feature masking in TabNet receives the feature representation of attentive transformer and makes decisions about selection. Decoder The decoding stage comprises two separate branches, which are illustrated in Figure 5. Each branch has a feature transformer and a FC block, which intends to reconstruct the encoder representation from different feature spaces by two branches. FIGURE 5Open in figure viewerPowerPoint The structure of the TabNet Decoder Let y ̂ tabnet ${\hat{y}_{{\rm{tabnet}}}}$ represent the predictions of TabNet model, the entire encoder-decoder process can be simply written as y ̂ tabnet = T a b N e t ( X GBDT ) \begin{equation}{\hat{y}_{{\rm{tabnet}}}} = TabNet({X_{{\rm{GBDT}}}})\end{equation} (17) 2.4.1 XGBoost XGBoost is the improved counterpart of the traditional boosting tree model. Boosting tree model is an efficient and widely used method to deal with the regression and classification tasks in machine learning [6, 11]. Newton's method is used in XGBoost to obtain the extreme value of the loss function, in which the loss function is expanded to the Taylor quadratic term, and an addition term is added to the loss function for regularization [15]. Therefore, the objective function is consisted of two parts during training. The first part is the loss of the gradient boosting algorithm, and the second part is the regularization term. The mathematical expression of the loss function can be written as L t = ∑ i = 1 n L ( y i , y ̂ i t − 1 + f t ( x ) ) + Ω ( f t ) \begin{equation}{L^t} = \sum_{i = 1}^n L (y_i,\hat{y}_i^{t - 1} + {f_t}(x)) + \Omega ({f_t})\end{equation} (18)where y ̂ i t − 1 $\hat{y}_i^{t - 1}$ is the sum of predicted values from time period 1 to t − 1; Ω ( f t ) $\Omega ({f_t})$ represents the regularization term and ft(x) is the forecasted values based on current tree model. Thus, y ̂ i t − 1 + f t ( x ) $\hat{y}_i^{t - 1} + {f_t}(x)$ is the predicted value of the time t. The residual is the difference between the real value ytarget and the predicted value y ̂ $\hat{y}$ . r e s i d u a l = y target − y ̂ xgboost \begin{equation}residual = {y_{{\rm{target}}}} - {\hat{y}_{{\rm{xgboost}}}}\end{equation} (19) As shown in Figure 6, XGBoost adopts an original system architecture by using a scalable end-to-end tree boosting. At each iteration of gradient boosting, the residuals will be used to correct the previous prediction. Therefore, the specified loss function can be optimized [11]. FIGURE 6Open in figure viewerPowerPoint The flow chart of XGBoost Let y ̂ xgboost ${\hat{y}_{{\rm{xgboost}}}}$ represent the predictions of XGBoost model, the entire XGBoost can be simply written as y ̂ xgboost = X G B o o s t ( X GBDT ) \begin{equation}{\hat{y}_{{\rm{xgboost}}}} = XGBoost({X_{{\rm{GBDT}}}})\end{equation} (20) 2.4.1 The ensemble of TabNet and XGBoost based on linear regression As stated in Sections 2.2.2 and 2.2.3, TabNet filters the existing features based on attention with mask and sends them to the FC layers to perform the training tasks. Meanwhile, XGBoost splits the features based on the improved boosting tree model. Generally, TabNet and XGBoost play distinct roles in the training stage and output different estimates. Thus, we adopt the linear regression to combine the results from both TabNet and XGBoost to render the final estimates [27]. The underlying process is depicted in Figure 7, y ̂ tabnet ${\hat{y}_{{\rm{tabnet}}}}$ and y ̂ xgboost ${\hat{y}_{{\rm{xgboost}}}}$ represent the predictions of TabNet and XGBoost model, respectively. ytarget denotes the target. FIGURE 7Open in figure viewerPowerPoint The ensemble forecasts via linear regression Both y ̂ tabnet ${\hat{y}_{{\rm{tabnet}}}}$ and y ̂ xgboost ${\hat{y}_{{\rm{xgboost}}}}$ are fed into the linear regression module as inputs, where the output is ytarget. As such, the weight coefficients w1 and w2 can be obtained automatically and efficiently. The advantage of linear regression is fast, simple and free of over-fitting. Here we show the fitting and forecasting processes In training w1 and w2 are calculated by linear regression using y ̂ tabnet ${\hat{y}_{{\rm{tabnet}}}}$ and y ̂ xgboost ${\hat{y}_{{\rm{xgboost}}}}$ . y target = w 1 y ̂ tabnet + w 2 y ̂ xgboost \begin{equation}{y_{{\rm{target}}}} = {\rm{ }}{w_1}{\hat{y}_{{\rm{tabnet}}}} + {w_2}{\hat{y}_{{\rm{xgboost}}}}\end{equation} (21) (2) In forecasting the final predictions are finished by w1, w2, y ̂ tabnet ${\hat{y}_{{\rm{tabnet}}}}$ and y ̂ xgboost ${\hat{y}_{{\rm{xgboost}}}}$ . y ̂ = w 1 y ̂ tabnet + w 2 y ̂ xgboost \begin{equation}\hat{y} = {\rm{ }}{w_1}{\hat{y}_{{\rm{tabnet}}}} + {w_2}{\hat{y}_{{\rm{xgboost}}}}\end{equation} (22) In the process of integration, the residual idea of ResNet [18] is applied to calibrate the prediction results of TabNet and XGBoost. The ResNet is to solve the gradient disappearance of the DNN. 2.5 Computational complexity of GTFD-XTNet One may notice that the complex configuration of the proposed GTFD-XTNet may give rise to huge computational burden in the training stage. As a matter of fact, the largest computational complexity comes from TabNet due to its encoder-decoder architecture. Within this TabNet, the Feature Transformer exists in both encoder and decoder (see Figures 2 and 5). By looking into its inner configuration in Figure 3, we found the FC and GLU may need large amount of time to tune the parameters. FC is composed of linear elements and non-linear functions. The time complexity of FC is O(n), n is the size of complexity problem. The GLU can be regarded as CNN with gating mechanism. In Equation (23), its time complexity is approximately equal to that of the CNN model. Therefore, the time complexity of a single convolutional layer is T i m e ∼ O ( M 2 ⋅ K 2 ⋅ C in ⋅ C out ) \begin{equation}Time\sim O({M^2}\centerdot {K^2}\centerdot {C_{{\rm{in}}}}\centerdot {C_{{\rm{out}}}})\end{equation} (23)where M is the side length of convolution kernel output feature map; K represents the edge length of each convolution kernel; Cin is the number of input channels of the convolution kernel. Cout is the number of output channels of the convolution kernel. Therefore, the computational complexity of GTFD-XTNet model is composed of the FC and GLU. 2.6 Algorithm The flowchart of the GTFD-XTNet implementation algorithm is elaborated by algorithm in Table 1. TABLE 1. Algorithm of GTFD-XTNet Algorithm Start 1) Input the all NWP candidate variables Xoriginal and corresponding power output ytarget as training samples 2) GTFD and normalization 3) Feature selection using GBDT 4) For each sample Establish a XTNet for each sample Training the XTNet for each sample End 5) Input the selected NWP features as test samples 6) GTFD and normalization 7) For each sample Power forecasting using the X