Accurate Wind Power Research Articles

BiLSTM-InceptionV3-Transformer-fully-connected model for short-term wind power forecasting

Accurate short-term wind power prediction can help grid managers optimize the transmission and storage of electricity from wind farms, avoiding energy wastage and unnecessary operating costs, and ensuring the stability and reliability of power supply. However, stochastic nature and uncertainty data pose challenges for accurate wind power prediction models. This study combines complementary ensemble empirical mode decomposition (CEEMD), discrete wavelet transform (DWT), bidirectional long short-term memory (BiLSTM), InceptionV3, Transformer, and fully connected neural networks for wind power prediction. The proposed prediction model can fully capture the nonlinear and dynamic characteristics of the wind speed data that are decomposed into intrinsic modal functions by CEEMD. Simultaneously, the prediction model can capture the local characteristics and cyclic variations of the data at different time scales based on fifth-order DWT. A dataset spanning 378 days from Natal is employed and the proposed model is compared with 19 other networks (e.g., ResNet, GoogleNet, SqueezeNet) and 15 other traditional regression algorithms (e.g., Gaussian process regression, least-angle regression, and nearest neighbors regression). The experimental results show that the designed BiLSTM-InceptionV3-Transformer-fully-connected model (BITFM) can reduce the mean absolute error by at least 38.75 % and the root mean squared error by at least 33.33 % compared with 19 other networks. Compared with 15 traditional regression prediction models, BITFM can reduce the mean absolute error by at least 57.41 % and the root mean squared error by at least 53.95 %. To further validate the prediction performance of BITFM, this study conducts tests employing data from the Santa Vitoria do Palmar. The experimental results indicate that BITFM consistently achieved the best performance metrics.

Enhancing the accuracy of wind power projections under climate change using geospatial machine learning models

This paper presents a geospatial artificial intelligence (GeoAI) approach for generating wind power projection maps employing various Machine Learning (ML) models. These models include Artificial Neural Network (ANN), Decision Tree (DT), Gaussian Process Regression (GPR), and Support Vector Regression (SVR), which collectively aim to provide insightful wind power forecasts under the effects of climate change. The framework considers different influential parameters affecting wind speed, including pressure gradient, temperature gradient, humidity, and topography. The study’s geographic focus is Cork City, Ireland. The investigation covers a historical period from 2000 to 2014 and extends to encompass two future climate scenarios, between 2015 and 2050. A comprehensive set of statistical skill scores is computed to gauge the models’ performance. The study’s findings underscore the efficacy of the ML models in generating dependable estimates of wind power fluctuations. Notably, the SVR model emerges as the frontrunner in performance across most pixels examined. Despite the inherent complexity of wind power dynamics, this research highlights that the SVR model can produce accurate wind power maps, even when operating with limited input data. The results emphasize the importance of considering influential factors in wind speed projections. This approach opens up promising avenues for improving the management of renewable energy resources.

A novel hybrid BWO-BiLSTM-ATT framework for accurate offshore wind power prediction

Accurate wind power forecasting is pivotal in facilitating the efficient integration of renewable energy sources into existing power grids. This study proposes a novel hybrid framework, termed BWO-BiLSTM-ATT, which synergistically combines the strengths of a bidirectional long short-term memory (BiLSTM) network, a self-attention mechanism, and the Beluga Whale Optimization (BWO) algorithm. The BiLSTM architecture, with its unique ability to capture bidirectional temporal dependencies, effectively models the intricate dynamics present in wind power time series data. Integrating the self-attention mechanism further enhances the model's performance by discerning and emphasizing the most salient features within the input data. Furthermore, employing the BWO algorithm optimizes the model's hyperparameters, ensuring optimal configuration and enhancing its predictive accuracy and generalization capabilities. The proposed BWO-BiLSTM-ATT framework was rigorously evaluated using real-world data from an offshore wind farm in Yangjiang City, China. Comparative analyses were conducted against several baseline algorithms, including persistence, ARIMA, SVR, and vanilla LSTM models. The results demonstrate the superior performance of the BWO-BiLSTM-ATT framework, achieving higher R-squared and explained variance scores, coupled with lower error metrics such as MSE, RMSE, MedAE, and MAE. Statistical significance tests further corroborated the substantial performance improvements offered by the proposed framework. The combination of advanced deep learning techniques and metaheuristic optimization algorithms embedded in the BWO-BiLSTM-ATT framework provides a robust and accurate solution for wind power forecasting, enabling efficient management and seamless integration of renewable energy resources into existing power grids.

Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models

Accurate wind power prediction helps to fully utilize wind energy and improve the stability of the power grid. However, existing studies mostly analyze key wind power-related features equally without distinguishing the importance of different features. In addition, single models have limitations in fully extracting input feature information and capturing the time-dependent relationships of feature sequences, posing significant challenges to wind power prediction. To solve these problems, this paper presents a wind power forecasting approach that combines feature weighting and a combination model. Firstly, we use the attention mechanism to learn the weights of different input features, highlighting the more important features. Secondly, a Multi-Convolutional Neural Network (MCNN) with different convolutional kernels is employed to extract feature information comprehensively. Next, the extracted feature information is input into a Stacked BiLSTM (SBiLSTM) network to capture the temporal dependencies of the feature sequence. Finally, the prediction results are obtained. This article conducted four comparative experiments using measured data from wind farms. The experimental results demonstrate that the model has significant advantages; compared to the CNN-BiLSTM model, the mean absolute error, mean squared error, and root mean squared error of multi-step prediction at different prediction time resolutions are reduced by 35.59%, 59.84%, and 36.77% on average, respectively, and the coefficient of determination is increased by 1.35% on average.

Enhancing wind power generation prediction using relevance assessment-based transfer learning

Accurate wind power generation forecasting can help build a reliable grid; however, the limited dataset makes accurate forecasting results a challenging work. This study introduces a relevant assessment-based transfer learning architecture to solve this problem. Linear fuzzy neighborhood mutual information is adopted to assess the relevance of the source domain selection. A convolutional structure with long-term memory architecture is designed as the deep learning model. The pre-trained model is transferred to the other wind turbines by calculating the linear fuzzy neighborhood mutual information. The proposed model avoids the lack of a dataset from analogous turbines. The simulation results indicate the proposed model surpasses the popular models in forecasting accuracy and exhibits superior time efficiency compared to popular deep-learning approaches.

Physics-informed reinforcement learning for probabilistic wind power forecasting under extreme events

With wind power penetration increases, accurate and reliable wind power forecasting is becoming gradually critical, and data driven model is a promising solution to implement this task. However, limited by deficient data samples under extreme conditions, scarcity of feature measurements fails to meet the number of training samples required, making the forecasting model exhibits low adaptability. This paper proposes a physics-informed reinforcement learning based method for probabilistic forecasting. Analytical physical expression of wind power output under extreme event is established to construct the error evaluation function for abrupt feature. Deep deterministic policy gradient-based quantile fitting model is then proposed, with abrupt feature embedded as the auxiliary input data for neural network. On this basis, parameter training technique under small data set is proposed to deal with the effect of extreme conditions on correction process of network. It extracts key historical transitions from experience replay pool to establish effective training samples, and model parameters are updated through the small-batch learning strategy to minimize long-term error feedback. Test result on the practical cold wave event of wind farms in China shows effectiveness of the proposed method.

A new short-term wind power prediction methodology based on linear and nonlinear hybrid models

Fast and accurate wind power prediction is of great significance for grid planning. However, wind power dataset tends to be highly stochastic and volatile, while showing more stable seasonal and cyclical changes, which is a linear and nonlinear superposition features, and it is difficult to use a single linear or nonlinear model to make accurate predictions. Considering these essential features of wind power data, a short-term wind power prediction method based on linear and nonlinear hybrid models is proposed in this paper. First, the complete ensemble empirical mode decomposition (CEEMD) is used to decompose and reconstruct the wind speed and direction in the original dataset to reduce the volatility. Then, to capture the linear features in the dataset, the autoregressive integrated moving average model with exogenous input variables (AIRMAX) is used for linear modeling, and the sparrow search algorithm optimized gated recurrent unit (SSA-GRU) is used to model the nonlinear features. At the same time, to improve the efficiency of model training, federated learning (FL) is utilized for distributed model training. Finally, the ARIMAX and SSA-GRU models are weighted and summed the prediction results to gain the final prediction results using equal weighted averaging (EWA), minimum sum of squares error (MSSE), and maximum grey relational analysis (MGRA), which are commonly used to compute the weights of the combined models, respectively. The wind power dataset from a wind farm in northwest China from January 1, 2022 to October 31, 2022 are used for experiments and analysis, and the results show that the root mean square error (RMSE), R-square (R2), and accuracy (ACC) of the proposed method are 11.944, 0.987, and 0.613, respectively, which are better than all the compared models. The main contribution of this paper is twofold, on the one hand, it indirectly proves that the wind power dataset is formed by the superposition of linear and nonlinear features, and on the other hand, it puts forward a theoretical model and research paradigm for predicting short-term wind power and presents a scientific basis for grid scheduling.

A reconstruction-based secondary decomposition-ensemble framework for wind power forecasting

Accurate wind power forecasting remains challenging due to the instability and volatility of wind power generation. Decomposition methods are widely used to improve forecasting performance by extracting complex fluctuation patterns from wind power series. However, previous decomposition-based models ignore the global interactions across sub-signals when forecasting the sub-signals separately and miss critical local details in sub-signals when modelling all the sub-signals in a single forecasting model. To address this issue, we propose a novel “Reconstruction-based Secondary Decomposition-Ensemble (RSDE)” framework for wind power forecasting, which simultaneously preserves the global interactions and local details. Firstly, an RSD method is adopted to extract fluctuation patterns from different frequency domains: decomposing the wind power by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), reconstructing the primary sub-signals from specific frequency domains by spectral clustering (SC) and further decomposing the reconstructed sub-signals by singular spectrum analysis (SSA). Secondly, temporal convolutional networks (TCNs) are applied to forecast each reconstructed sub-signal by fitting the corresponding secondary decomposed sub-signals, which could effectively capture the local and global features from specific frequency domains. Finally, an ensemble strategy with error correction is adopted to obtain the final forecasting results by combining the forecasted reconstructed sub-signals and corresponding error forecasting results. Four wind power datasets with different time resolutions are introduced to evaluate the forecasting performance. The experimental results demonstrate that the proposed RSDE framework consistently outperforms the benchmark models. Moreover, the proposed sub-signal modelling strategy improves the forecasting performance by more than 30% on average, and the ensemble strategy with error correction improves the forecasting performance by about 10% on average.

Wind power prediction through acoustic data-driven online modeling and active wake control

Accurate online prediction of wind power is essential for the reliable operation of power systems. However, the precision of wind turbine power forecasts is often hindered by wake effects and inadequate online modeling capabilities. To tackle this issue, this paper proposes a data-driven online prediction method for wind turbine power, leveraging acoustic data for training. The approach involves a neural network that learns acoustic feature changes related to wind output power, by enhancing the kernel extreme learning machine (KELM) technique and applying data augmentation. Additionally, to address the wake effects in real wind fields, active wake control is integrated, considering that most wind turbines operate in the wake of upstream turbines. Experiments using actual wind tunnel measurement data were conducted to assess the performance of the proposed method. The findings demonstrate that this method surpasses other techniques without increasing computational costs. The mean absolute error (MAE) of the proposed method was only 0.4111 and 0.4302, and the root mean square error (RMSE) was just 0.4868 and 0.5195, respectively, in both experiments. Moreover, the proposed acoustic data-driven online power prediction method proves stable in high background noise environments and achieves accurate wind power prediction with just a 10% data sample following the implementation of the active wake control strategy. This work significantly contributes to the efficient utilization of wind energy resources.

A novel BiGRU multi-step wind power forecasting approach based on multi-label integration random forest feature selection and neural network clustering

Accurate wind power forecasting helps to carry out effective scheduling and scientific management of wind power, and improve the security and reliability of the power grid. However, the intermittency, volatility and instability of wind energy make wind power forecasting challenging. Therefore, in order to improve the accuracy and stability of wind power forecasting, this paper proposes a bidirectional gated recurrent unit (BiGRU) multi-step wind power forecasting approach based on multi-label integration random forest (MLRF) feature selection and neural network clustering (NNClustering). The proposed MLRF method extends the applicability of random forest through multiple criteria and enables feature selection for multi-step forecasting tasks of multi-factor time series to obtain optimal input features and time steps, which reduces the computational cost and improves the generalization ability of the model. The proposed NNClustering method establishes a novel convolution-based clustering structure and adjusts the parameters by gradient descent method to obtain the optimal clustering centers, and the robust data applicability of the method is validated in multiple seasonal experiments. The WOA-BiGRU forecasting model is constructed separately for each cluster, which reduces the modeling difficulty and better extracts the characteristics. The BiGRU model extracts more efficient characteristics by processing sequences in both directions and the important parameters of BiGRU are optimized by the whale optimization algorithm (WOA) to obtain the optimal forecasting model. Experimental results over multiple seasons show that the proposed hybrid approach has good forecasting performance and robustness, which provides a novel and efficient solution for wind power forecasting.

Prediction of wind power ramp events via a self-attention based deep learning approach

With the increasing popularity of wind power generation, the importance of accurate wind power ramp events (WPRE) prediction has been further emphasized. At present, WPRE prediction model based on deep learning shows superior performance, which does not need to fully grasp the operation of wind power generation, but ignores the interdependence mechanism of prediction error along the input characteristics of the neural network. This paper develops a self-attention (SAM) and wavelet transform (WT) based hybrid 1DCNN (one-dimensional convolutional neural network) and LSTM (long short term memory networks) for WPRE forecasting. In the proposed model, WT effectively separates the middle and high frequency noise signals in wind power and SAM can redistribute the neural weights in 1DCNN-LSTM, then 1DCNN-LSTM further extracts the space-time information of effective wind power. Finally, the performance of the forecast model proposed in this paper is tested by using the wind power data from the Belgian ELIA website. The experimental results show that, compared with the other six competition models, the proposed model effectively suppresses the influence of randomness, volatility and uncertainty of wind power on the recognition of ramp events. Furthermore, through quantitative analysis, this paper explains the reason of SAM is superior to traditional AM in terms of their application object and weight control range in the proposed model.

Enhancing wind power prediction with self-attentive variational autoencoders: A comparative study

Accurate wind power prediction is critical for efficient grid management and the integration of renewable energy sources into the power grid. This study presents an effective deep-learning approach that improves short-term wind power forecasting accuracy. The method incorporates a Variational Autoencoder (VAE) with a self-attention mechanism applied in both the encoder and decoder. This empowers the model to leverage VAE's strengths in time-series modeling and nonlinear approximation while focusing on the most relevant features within the wind power data. The effectiveness of this approach is evaluated through a comprehensive comparison with eight established deep learning methods, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Bidirectional LSTMs (BiLSTMs), Convolutional LSTMs (ConvLSTMs), Gated Recurrent Units (GRUs), Stacked Autoencoders (SAEs), Restricted Boltzmann Machines (RBMs), and vanilla VAEs. Real-world data from five wind turbines in France and Turkey is used for the evaluation. Five statistical metrics are employed to quantitatively assess the prediction performance of each method. The results indicate that the SA-VAE model consistently outperformed other models, achieving the highest average R2 value of 0.992, demonstrating its superior predictive capability compared to existing techniques.

Ultra-short-term wind power forecasting based on a dual-channel deep learning model with improved coot optimization algorithm

The large-scale integration of wind power to the grid poses some potential challenges to the power system. Accurate wind power forecasts reduce the impact of the nonlinearities and volatility of wind power generation. A two-channel deep learning model based on an improved coot optimization algorithm (ICOA) is proposed. First, the use of long short-term memory (LSTM) builds a channel for the extraction of chronological characteristics of historical power. Second, a temporal convolutional network (TCN) is adopted as a hybrid feature-extraction channel for multi-dimensional meteorological data, and by incorporating the self-attention mechanism (SA), the ability of TCN to extract internal information from meteorological features is enhanced. Finally, the ICOA that introduces nonlinear decision factor, adaptive dynamic boundary, and Cauchy mutation is used to optimize the model hyperparameters. The simulation analysis is carried out on the winter and summer measured data of a wind farm in Xinjiang. The results show that compared with the traditional LSTM model, the root mean square error and the mean absolute error of the proposed method are reduced by 10.35 % and 16.27 % on average, respectively, and the prediction accuracy is higher than that of other comparative models, which verifies the superiority of our proposed model.

Proactive failure warning for wind power forecast models based on volatility indicators analysis

With the promotion of low-carbon models, the proportion of wind power energy has significantly increased. Accurate wind power forecasting is of great significance for the scheduling of power systems. Previous studies often focused on improving forecasting accuracy, neglecting the issue of model failure (abnormally large forecast error occurring). However, forecasting model failure brings significant misleading information to the scheduling of the power system. To address this issue, this paper firstly analyzes the error distribution of prediction models under various neural networks (CNN, CNN-GRU, DNN, ConvLSTM, ELM, GBDT, AR, TREE and XGBoost) and various loss function (MAE, MSE, MLSE and Log-cosh) combinations. Subsequently, based on the Backward cloud generator and Pearson correlation analysis, the paper confirmed that the forecasting errors mainly come from the volatility of the wind power sequence itself, rather than the types and structures of the models. Finally, the paper uses variation and variance to assist Bi-LSTM in 1-h-ahead early warning of forecasting model failure under two kinds of thresholds, and has achieved excellent reliability and accuracy. The warned models show obvious failure situations, both in terms of single error peaks and cumulative errors.

Short-term wind farm cluster power point-interval prediction based on graph spatio-temporal features and S-Stacking combined reconstruction

Wind energy is becoming increasingly competitive, Accurate and reliable multi-engine wind power forecasts can reduce power system operating costs and improve wind power consumption capacity. Existing research on wind power forecasting has neglected the importance of interval forecasting using clusters of wind farms to capture spatial characteristics and the objective selection of forecasting sub-learners, leading to increased uncertainty and risk in system operation. This paper proposes a new “decomposition-aggregation-multi-model parallel prediction” method. The data set is pre-processed by a decomposition-aggregation strategy and spatial feature extraction, and then a Stacking model with multiple parallel sub-learners selected by bootstrap method is used for point and interval forecasting. Experiments and discussions are conducted based on 15-min resolution wind power data from a cluster dataset of a wind farm in northwest China. The experimental results indicate that the method achieves higher accuracy and reliability in both point prediction and interval prediction than other comparative models, with a root mean square error value of 7.47 and an average F value of 1.572, which can provide a reliable reference for power generation planning from wind farm clusters.

Ultra-short-term wind power forecasting method based on multi-variable joint extraction of spatial-temporal features

Accurate and reliable wind power forecasting is imperative for wind power stations' stable and efficient operation. Information such as wind speed and wind direction in the same wind field has spatial-temporal differences. Considering the spatial-temporal changes in wind fields can improve model prediction accuracy. However, existing methods suffer from limited ability to capture correlation features among variables, information loss in spatial-temporal feature extraction, and neglect short-term temporal features. This paper introduces a novel ultra-short-term wind power forecasting method based on the combination of a deep separable convolutional neural network (DSCNN) and long- and short-term time-series network (LSTNet), incorporating maximum information coefficient (MIC) to realize multi-variable joint extraction of spatial-temporal features. The method utilizes MIC to jointly analyze and process the multi-variate variables before spatial-temporal feature extraction to avoid information redundancy. The spatial features between input variables and wind power are extracted by deep convolution and pointwise convolution in DSCNN. Then, a convolutional neural network and gated recurrent unit in LSTNet are combined to capture long-term and short-term temporal features. In addition, an autoregressive module is employed to accept features extracted by MIC to enhance the model's learning of temporal features. Based on real datasets, the performance of models is validated through comprehensive evaluation experiments such as comparison experiments, ablation experiments, and interval prediction methods. The results show that the proposed method reduces mean absolute error by up to 4.66% and provides more accurate prediction intervals, verifying the accuracy and effectiveness of the proposed method.

Wind power forecasting based on a novel gated recurrent neural network model

Accurate wind power forecasting is crucial for upgrading the operation and maintenance of wind turbines and the effectiveness in wind power dispatching. In this work, density-based spatial clustering of applications with noise (DBSCAN) is adopted to exclude outliers, and is examined to yield top-quality data filtering for neural networks when implemented on supervisory control and data acquisition (SCADA) data from a 7-MW wind turbine. Apart from features, such as wind and rotor speeds, time is a vital feature involved with synchronous or asynchronous events. Thus, a model-agnostic vector representation for time, Time2Vec (T2V), is used. This can be embedded with neural network architectures, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The novel neural network model, T2V-GRU, concatenates T2V into vanilla GRU, replacing the notion of time vector by T2V. Forecasting results are evaluated by mean absolute error (MAE), root mean squared error (RMSE), and accuracy. In comparison to T2V-LSTM, LSTM and GRU, the proposed T2V-GRU model in combination with DBSCAN refinement has superior forecasting results for the 7-MW turbine, yielding maximum forecasting accuracy (93.12 %) and minimal prediction errors in terms of MAE (286.10 kW) and RMSE (489.54 kW), while simultaneously reducing the computational cost and time.

Hybrid model based on similar power extraction and improved temporal convolutional network for probabilistic wind power forecasting

Accurate wind power generation forecasting is of great significance to improve the operation of power system. Probabilistic forecasting has a higher application value in power grid because it can provide more abundant forecasting information than deterministic forecasting. In addition, multi-step forecasting can provide forecasting results in a longer time range, so that decision makers can make longer-term planning and strategic arrangements. In this paper, we propose a novel multi-step improved temporal convolutional network based on quadratic spline quantile function (MITCN-QSQF) for probabilistic wind power forecasting. First, we combine maximum information coefficient, Gaussian similarity and adaptive resample to propose an effective similar power generation feature extraction method (MGR) for power generation. Then the temporal convolutional network is improved to construct the multi-step time series forecasting model MITCN. By combining the proposed model and the powerful probabilistic forecasting method quadratic spline quantile function (QSQF), high-quality probabilistic forecasting of wind power is achieved. Through comprehensive simulations on an open-source dataset, the superiority and efficiency of the proposed method are verified. Compared with some advanced benchmarks, the proposed model can obtain more accurate deterministic and probabilistic forecasting results.

Short-term wind power prediction based on improved sparrow search algorithm optimized long short-term memory with peephole connections

Accurate short-term wind power prediction is of great significance for the scheduling and management of wind farms. This paper proposes a model for short-term wind power prediction. Firstly, on the basis of traditional long short-term memory network, the peephole connections is added. The improved long short-term memory network is more stable compared to traditional long short-term memory neural networks and is suitable for regression prediction. Secondly, chaotic mapping, adaptive weights, Cauchy mutation, and opposition-based learning strategies are introduced to improve the sparrow search algorithm, and applied to optimize the four hyper-parameters of the long short-term memory network, greatly improving the prediction accuracy of the network. The effectiveness of the model is validated using two short-term wind power datasets with sampling times of 10 and 30 minutes respectively, combined with some fitting curves and performance indicators. The comparison results indicate that the proposed short-term wind power prediction model has high prediction accuracy.

A novel hybrid model based on multiple influencing factors and temporal convolutional network coupling ReOSELM for wind power prediction

Highly accurate wind power prediction is important for the operation and management of wind farms, energy utilization and power supply. In recent years, in order to further improve prediction accuracy, some researchers have considered utilizing factors such as wind speed and wind direction to establish prediction models. However, this often results in input redundancy due to a lack of technical means. In this paper, a multivariate prediction model for wind power based on variational mode decomposition (VMD), fuzzy entropy (FE), partial auto-correlation function and cross-correlation function (PACF&CCF, abbreviated as PC), improved artificial hummingbird algorithm (IAHA), temporal convolutional network (TCN), and regularized online sequential extreme learning machine (ReOSELM) is proposed. To address the nonlinearity and non-stationarity of the original wind power time series and decrease computational costs, the VMD combined with FE data preprocessing technique is employed. Considering the information redundancy in the multivariate data, the PC technique is utilized to conduct correlation analysis on the input data and select highly correlated features as inputs. When the input is multivariate and high-dimensional, the ReOSELM model struggles to achieve satisfactory prediction performance. Therefore, a new model based on TCN-ReOSELM is constructed. Additionally, to overcome the drawbacks of manual parameter adjustment, an IAHA algorithm is proposed, which integrates Latin hypercube sampling and chaotic local search strategies, to optimize the crucial parameters of TCN-ReOSELM. The experimental results demonstrate that the multivariate method proposed in this paper reduces the RMSE by 31% compared to the univariate method. Specifically, the proposed VMD-FE-PC-TCN-ReOSELM model reduces the RMSE, MAE, and SMAPE by 60–64%, 55–58%, and 10–36%, respectively, compared to the PC-TCN-ReOSELM model.