Short-term Wind Speed Forecasting Research Articles

Enhanced prediction model of short-term sea surface wind speed: A multiscale feature extraction and selection approach coupled with deep learning technique

Accurate prediction of short-term sea surface wind speed is essential for maritime safety and coastal management. Most conventional studies encounter challenges simply in analyzing raw wind speed sequences and extracting multiscale features directly from the original received data, which result in lower efficiency. In this paper, an enhanced hybrid model based on a novel data assemble method for original received data, a multiscale feature extraction and selection approach, and a predictive network, is proposed for accurate and efficient short-term sea surface wind speed forecasting. Firstly, the received original data including wind speed are assembled into correlation matrices in order to uncover inherent associations over varied time spans. Secondly a novel Multiscale Wind-speed Feature-Enhanced Convolutional Network (MW-FECN) is designed for efficient and selective multiscale feature extraction, which can capture comprehensive characteristics. Thirdly, a Random Forest Feature Selection (RF-FS) is employed to pinpoint crucial characteristics for enhanced prediction of wind speed with higher efficiency than the related works. Finally, the proposed hybrid model utilized a Bidirectional Long Short-Term Memory (BiLSTM) network to achieve the accurate prediction of wind speed. Experimental data are collected in Weihai sea area, and a case study consist of five benchmarks and three ablation models is conducted to assess the proposed hybrid model. Compared with the conventional methods, experiment results illustrate the effectiveness of the proposed hybrid model and demonstrate effective balancing prediction accuracy and computational time. The proposed hybrid model achieves up to a 28.45% MAE and 27.27% RMSE improvement over existing hybrid models.

Wind-Speed Multi-Step Forecasting Based on Variational Mode Decomposition, Temporal Convolutional Network, and Transformer Model

Reliable and accurate wind-speed forecasts significantly impact the efficiency of wind power utilization and the safety of power systems. In addressing the performance enhancement of transformer models in short-term wind-speed forecasting, a multi-step prediction model based on variational mode decomposition (VMD), temporal convolutional network (TCN), and a transformer is proposed. Initially, the Dung Beetle Optimizer (DBO) is utilized to optimize VMD for decomposing non-stationary wind-speed series data. Subsequently, the TCN is used to extract features from the input sequences. Finally, the processed data are fed into the transformer model for prediction. The effectiveness of this model is validated by comparison with six other prediction models across three datasets, demonstrating its superior accuracy in short-term wind-speed forecasting. Experimental findings from three distinct datasets reveal that the developed model achieves an average improvement of 52.1% for R2. To the best of our knowledge, this places our model at the leading edge of wind-speed prediction for 8 h and 12 h forecasts, demonstrating MSEs of 1.003 and 0.895, MAEs of 0.754 and 0.665, and RMSEs of 1.001 and 0.946, respectively. Therefore, this research offers significant contributions through a new framework and demonstrates the utility of the transformer in effectively predicting short-term wind speed.

A novel ensemble system for short-term wind speed forecasting based on hybrid decomposition approach and artificial intelligence models optimized by self-attention mechanism

Accurate wind speed forecasting is crucial for wind energy development and utilization. The non-linearity and non-stationarity of the wind speed leads to difficulties in its prediction. Recent years, plentiful forecasting approaches have been developed to improve the short-term wind speed forecasting. Among them, the ensemble system that consists of several sub-models is highly competitive. However, most of ensemble systems employ traditional data pre-processing method and integrate the outputs of sub-models with fixed weight assignment, which severely constrains the forecasting performance. To address these prevailing problems and further enhance the forecasting accuracy and stability, a novel ensemble system is proposed in this study, which comprising four modules: a data pre-processing module, an optimization module, a multiscale prediction module and a combination module. A hybrid decomposition approach is developed to fully exploit the changing modes of wind speed series in data pre-processing module. The multiscale prediction module employs advanced artificial intelligence models to conduct forecasting with the assistance of the optimization module. Finally, the self-attention mechanism is used to combine the outputs of sub-models. Measured hourly wind speed from four stations are adopted as datasets, and four metrics are used to evaluate the forecasting performance. Three comprehensive experiments are designed to explore the functionality and validate the predictive capability of the proposed ensemble system. The experimental results indicate that the proposed ensemble system significantly outperformed single models. The superiority of the proposed ensemble system is also demonstrated in comparisons with hybrid models and other ensemble systems. Overall, the proposed ensemble system can provide more accurate and reliable forecasting for the scheduling and management of wind power systems than other benchmark models.

Wavelet decomposition and neural networks: a potent combination for short term wind speed and power forecasting

The forecast of wind speed and the power produced from wind farms has been a challenge for a long time and continues to be so. This work introduces a method that we label as Wavelet Decomposition-Neural Networks (WDNN) that combines wavelet decomposition principles and deep learning. By merging the strengths of signal processing and machine learning, this approach aims to address the aforementioned challenge. Treating wind speed and power as signals, the wavelet decomposition part of the model transforms these inputs, as appropriate, into a set of features that the neural network part of the model can ingest to output accurate forecasts. WDNN is unconstrained by the shape, layout, or number of turbines in a wind farm. We test our WDNN methods using three large datasets, with multiple years of data and hundreds of turbines, and compare it against other state-of-the-art methods. It’s very short-term forecast, like 1-h ahead, can outperform some deep learning models by as much as 30%. This shows that wavelet decomposition and neural network are a potent combination for advancing the quality of short-term wind forecasting.

Short-term wind speed forecasts through hybrid model based on improved variational mode decomposition

ABSTRACT Scientific and accurate wind predictions are the basis for exploiting and utilizing wind energy. Combining the VMD and KELM, this research proposes a new hybrid model to capture the pattern of wind speed change, the parameters of which are optimized by the Spotted Hyena Optimizer(SHO) and Seagull Optimization Algorithm(SOA), respectively. The VMD is optimized by the SHO to achieve the purpose of adaptive decomposition of historical wind speed time series. After that, the parameters of the KELM are optimized by the SOA. Then, the decomposed wind speed series is input into the optimized KELM. The output prediction Intrinsic Mode Functions(IMFs) is added up to obtain the short-term wind speed prediction results. The measured wind speeds for four seasons were selected as the case study for the proposed model. The prediction results are compared with the measured wind speed series to verify the accuracy and reliability of the model. Besides, the prediction accuracy and stability of the proposed model are better than traditional prediction models, with stronger performance and lower computational cost. The results of case studies indicate that the proposed model can satisfy the need for actual applications.

A short-term wind speed prediction method based on the BLS–RVM hybrid model

Abstract The study introduces a novel approach to short-term wind speed forecasting, which amalgamates statistical and machine learning techniques through the utilization of a hybrid model consisting of the broad learning system (BLS) and the relevance vector machine (RVM), to account for the nonstationary characteristics of wind speed data. Firstly, the initial wind speed time series is preprocessed using singular spectrum analysis to achieve noise reduction. Subsequently, the denoised wind speed time series is predicted using generalized learning system (BLS), and the prediction error is obtained. Finally, the prediction error of BLS is further predicted using RVM, and the final prediction result is obtained by combining it with the prediction results of BLS. By integrating multiple algorithms, this novel wind speed forecasting hybrid model improves prediction accuracy and can adapt to different wind speed characteristics and complex wind speed fluctuations. Through a case study, we find that this model outperforms other comparative models in terms of prediction, fully demonstrating its superiority.

Performance enhancement of short-term wind speed forecasting model using Realtime data.

The ever-increasing demand for electricity has presented a grave threat to traditional energy sources, which are finite, rapidly depleting, and have a detrimental environmental impact. These shortcomings of conventional energy resources have caused the globe to switch from traditional to renewable energy sources. Wind power significantly contributes to carbon-free energy because it is widely accessible, inexpensive, and produces no harmful emissions. Better and more efficient renewable wind power production relies on accurate wind speed predictions. Accurate short-term wind speed forecasting is essential for effectively handling unsteady wind power generation and ensuring that wind turbines operate safely. The significant stochastic nature of the wind speed and its dynamic unpredictability makes it difficult to forecast. This paper develops a hybrid model, L-LG-S, for precise short-term wind speed forecasting to address problems in wind speed forecasting. In this research, state-of-the-art machine learning and deep learning algorithms employed in wind speed forecasting are compared with the proposed approach. The effectiveness of the proposed hybrid model is tested using real-world wind speed data from a wind turbine located in the city of Karachi, Pakistan. Moreover, the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are used as accuracy evaluation indices. Experimental results show that the proposed model outperforms the state-of-the-art legacy models in terms of accuracy for short-term wind speed in training, validation and test predictions by 98% respectively.

Two-stage decomposition and temporal fusion transformers for interpretable wind speed forecasting

Contemporary wind speed prediction research methodologies often employ two-stage decomposition preprocessing techniques to leverage the temporal correlation of wind speed. However, they frequently neglect to investigate the interpretability within the wind speed prediction model. To this end, a novel and interpretable hybrid forecasting model that combines two-layer decomposition, adaptive differential evolution with optional external archive (JADE), and temporal fusion transformers (TFT) is proposed. Primarily, on the basis of the linear-nonlinear decomposition criterion, a set of subcomponents is obtained using two-stage decomposition to fully extract the wind speed series prediction information for high-fluctuation and multi-resolution modes. Utilizing the JADE algorithm for intelligent and efficient optimization of parameter combinations in the TFT model guarantees the stability and reliability of the prediction model. Later, the obtained two-layer decomposition subseries are used as historical variables, and the meteorological and temporal data are entered into the TFT model as future known inputs. Empirical studies show that the proposed model demonstrates remarkable suitability and effectiveness in short-term wind speed forecasting. The utilization of the interpretable model has catalyzed significant advancements in wind speed prediction, while the analysis of its interpretable results empowers managers in formulating effective policies.

A comprehensive wind speed prediction system based on intelligent optimized deep neural network and error analysis

At present, wind power is one of the most promising clean energy sources. Due to its high variability, accurate wind speed forecasting is an essential part of the wind power industry. This paper proposes a comprehensive forecasting system consisting of three parts: Firstly, the novel SC-EWT wind speed signal reconstruction system proposed in this article can effectively extract information from wind speed signals. Secondly, the QBGA-GRU model is used for signal reconstruction prediction, where QBGA is an improved genetic algorithm proposed in this article that can improve prediction accuracy. Finally, the IPSO-BLS algorithm is used to quickly analyze and predict prediction errors and further correct the predicted results. The system can provide users with adaptive signal decomposition, high-precision deep learning prediction and error analysis functions to achieve efficient and fast short-term wind speed forecasting, which can be used in the fields of wind power integration deployment and wind turbine early warning. The experiments show that the proposed prediction strategy has good predictive ability for wind speed samples in two regions, and its error is smaller than that of commonly used intelligent algorithms. Compared with other models, the proposed comprehensive prediction strategy has better predictive performance and smaller prediction errors than other combination models.

Short-term wind speed combined forecasting model based on multi-decomposition algorithms and frameworks

The normal operation of the electrical grid is directly impacted by the precision of short-term wind speed forecasting. However, nonlinearities in wind velocities make their forecasting challenging. In previous research, the singularity of decomposition methods and frameworks in the model hampered the precision of forecasting. This study proposes an integration model that incorporates multiple decomposition algorithms and frameworks to address this issue. In the preprocessing stage, the model employs multiple decomposition algorithms to decompose the original wind speed data, which effectively reduces the nonlinearity of the wind speed data. Meanwhile, the multiscale decomposition gives more diverse inputs to the sub-model. During the forecasting stage, we utilized multiple frameworks in combination with the new hybrid model to construct sub-models. Using multiple frameworks, we can fully explore the potential information and relationships among sub-sequences of varying frequencies and their connections with related variables. The Proposed Hybrid model improves the forecasting performance of sub-models. Lastly, the sub-models are recombined using a deep learning nonlinear combination strategy to obtain the final result. The experimental data were obtained from real wind farms. The final forecasts were evaluated using four evaluation metrics. The RMSE of the combined model is 0.1583, the MAE is 0.1241, the MAPE (%) is 1.9119, and the R2 is 0.9948. The proposed combined forecasting model outperforms all baseline models and it makes wind speed forecasting more accurate. In the future, the model could help wind farm maintenance and facilitate power system development.

Short-Term Wind Speed and Power Forecasting for Smart City Power Grid With a Hybrid Machine Learning Framework

To address foreseeable challenges during the penetration of wind energy into the power grid, including accurate wind power forecasting and smart power generation scheduling, this study proposes a novel short-term wind speed forecasting model, named EMD-KM-SXL, which is based on empirical mode decomposition (EMD), K-means clustering and machine learning, and a new two-stage short-term wind power forecasting model based on wind speed forecasting and wind power curve modeling. The former wind speed forecasting model regards historical wind speed observations as model inputs, and the latter power forecasting model utilizes knowledge augmentation, introducing wind power conversion relationship, environmental factors as well as wind power system status parameters. In the proposed wind speed forecasting model, three machine learning models, including support vector regressor, XGBoost regressor, and Lasso regressor, are employed to forecast three types of frequency components that are generated via EMD and K-means clustering. Then, the wind power curve model is utilized to compute potential outpower based on the predicted wind speed, which is regarded as the first stage of the proposed wind power forecasting model. In the second stage, environmental factors and wind power system status parameters are introduced and an artificial neural network model, considering preliminary predicted power, environmental factors, and wind power system status parameters as model inputs, is built to make final power prediction. Computational results show that proposed models achieve the best performance in terms of wind speed and power forecasting over different forecasting horizons ranging from 10 to 40 minutes, compared with benchmarking methods.

Probabilistic short-term wind speed forecasting using a novel ensemble QRNN

Neural Networks are widely used as the intelligent tool for short-term wind speed forecasting. Owing to the inherent randomness and intermittency of the wind, it is hard to ensure sufficient forecasting precision when using single conventional Network models to conduct deterministic forecasting. Thanks to the recent achievements of ensemble methods, a novel ensemble Quantile Regression Neural Network (QRNN) based on Wavelet decomposition (WD) and least absolute shrinkage and selection operator (LASSO) for probabilistic short-term wind speed forecasting is developed here. The signal processing technology, feature extraction method and predicting model are ingeniously combined in the proposed ensemble QRNN. More specifically, the WD is utilized to extract the signal features into a collection of easy-to-analyze subseries. LASSO regression is combined with the QRNN to conduct the robust prediction. Finally, the kernel density estimation method is adopted to further enhance the forecasting precision. The forecasting performance between the proposed ensemble QRNN and several benchmark models are evaluated using a piece of field measured 10-min mean wind speed data. Results show that the proposed ensemble model can adequately enhance the performance of short-term wind speed forecasting, which can also quantify and reduce the uncertainty of the prediction results.

Twin extreme learning machine based on heteroskedastic Gaussian noise model and its application in short-term wind-speed forecasting

Extreme learning machine (ELM) has received increasingly more attention because of its high efficiency and ease of implementation. However, the existing ELM algorithms generally suffer from the drawbacks of noise sensitivity and poor robustness. Therefore, we combine the advantages of twin hyperplanes with the fast speed of ELM, and then introduce the characteristics of heteroscedastic Gaussian noise. In this paper, a new regressor is proposed, which is called twin extreme learning machine based on heteroskedastic Gaussian noise (TELM-HGN). In addition, the augmented Lagrange multiplier method is introduced to optimize and solve the presented model. Finally, a significant number of experiments were conducted on different data-sets including real wind-speed data, Boston housing price dataset and stock dataset. Experimental results show that the proposed algorithms not only inherits most of the merits of the original ELM, but also has more stable and reliable generalization performance and more accurate prediction results. These applications demonstrate the correctness and effectiveness of the proposed model.

Short-Term Wind Speed Forecasting Using Nonlinear Autoregressive Neural Network: A Case Study in Kocaeli-Türkiye

Recently, wind energy has been utilized globally as a renewable, sustainable, and eco-friendly energy source. However, wind energy’s unpredictable and stochastic nature influences its entry into the national electrical grid. An effective wind speed prediction is required to meet these challenges. In this article, the Nonlinear Autoregressive Neural Network (NARNN) model is used and investigated for short-term wind speed forecasting by taking a dataset from the Kandıra wind farm in Kocaeli- Türkiye. The crux of the paper is to improve the actual application of the existing NARNN model with factual data using a different number of neurons of the hidden layer, delays, and training functions in the learning phase called the model’s hyperparameters. The mean squared error (MSE) and determination coefficient (R2) are used as performance measures. As a result, the hyperparameter optimization for wind speed prediction using the NARNN increased the forecasting performance. Suggested NARNN model is compared with its exogenous version (NARXNN) using three extra inputs. It is observed that NARNN is not falling behind NARXNN because they provide close results, and NARNN has been shorter to run. Likewise, the learning algorithms were also compared, and it turned out that Bayesian Regularization (BR) is the best learning algorithm. Still, Levenberg Marquardt (LM) algorithm is much faster to execute and provides close results to BR.

Weather image-based short-term dense wind speed forecast with a ConvLSTM-LSTM deep learning model

Short-term wind speed predication is of great significance for scholars (e.g., understanding wind profiles), practitioners (e.g., building energy management), regulators (e.g., urban microclimate regulation), and even the general public. Current wind speed forecasting methods either generate sparse predictions or occur high cost. This paper reports a novel, inexpensive framework to forecast urban local dense wind speed. The central tenet is a convolutional long short-term memory (ConvLSTM) and LSTM combinatorial deep learning model to learn the features of input historical weather image series coupled with spatial-temporal correlations. The model was trained and tested using Hong Kong datasets. The feasibility and effectiveness of the proposed model are verified and compared with parallel models under different criteria, including mean absolute error (MAE), root mean square error (RMSE) and R-squared (R2). The experimental results show that: (1) the proposed ConvLSTM-LSTM deep learning model can effectively forecast wind speed regardless of location; (2) the overall MAE, RMSE, and R2 value of the proposed model are improved by 14.84%, 15.04%, and 7.51%, respectively, compared to the ConvLSTM-full connected (ConvLSTM-FC) model, and by 22.12%, 22.80%, and 12.24%, respectively, compared to the convolutional neural network-LSTM (CNN-LSTM) model; and (3) compared with parallel models, the proposed model has better performance in predicting wind speed series with large amplitude variations and rapid frequency changes.

Extreme learning Kalman filter for short-term wind speed prediction

Accurate prediction of wind speed is critical for realizing optimal operation of a wind farm in real-time. Prediction is challenging due to a high level of uncertainty surrounding wind speed. This article describes use of a novel Extreme Learning Kalman Filter (ELKF) that integrates the sigma-point Kalman filter with the extreme learning machine algorithm to accurately forecast wind speed sequence using an Artificial Neural Network (ANN)-based state-space model. In the proposed ELKF method, ANNs are used to construct the state equation of the state-space model. The sigma-point Kalman filter is used to address the recursive state estimation problem. Experimental data validations have been implemented to compare the proposed ELKF method with autoregressive (AR) neural networks and ANNs for short-term wind speed forecasting, and the results demonstrated better prediction performance with the proposed ELKF method.

Short-Term Wind Speed Forecasting Based on the EEMD-GS-GRU Model

To improve the accuracy of short-term wind speed forecasting, we proposed a Gated Recurrent Unit network forecasting method, based on ensemble empirical mode decomposition and a Grid Search Cross Validation parameter optimization algorithm. In this study, first, in the process of decomposing, the set empirical mode of decomposition was introduced to divide the wind time series into high-frequency modal, low-frequency modal, and trend modal, using the Pearson correlation coefficient. Second, during parameter optimization, the grid parameter optimization algorithm was employed in the GRU model to search for the combination of optimal parameters. Third, the improved GRU model was driven with the decomposed components to predict the new components, which were used to obtain the predicted wind speed by modal reorganization. Compared with other models (i.e., the LSTM, GS-LSTM, EEMD-LSTM, and the EEMD-GS-LSTM), the proposed model was applied to the case study on wind speed of a wind farm, located in northwest China. The results showed that the presented forecasting model could reduce the forecasting error (RMSE) from 1.411 m/s to 0.685 m/s and can improve the accuracy of forecasts. This model provides a new approach for short-term wind speed forecasting.

Application of Augmented Echo State Networks and Genetic Algorithm to Improve Short-Term Wind Speed Forecasting

The large-scale integration into electrical systems of intermittent power-generation sources, such as wind power plants, requires greater efforts and knowledge from operators to keep these systems operating efficiently. These sources require reliable output power forecasts to set up the optimal operating point of the electrical system. In previous research, the authors developed an evolutionary approach algorithm called RCDESIGN to optimize the hyperparameters and topology of Echo State Networks (ESN), and applied the model in different time series forecasting, including wind speed. In this paper, RCDESIGN was modified in some aspects of the genetic algorithm, and now it optimizes an ESN with augmented states (ESN-AS) and has been called RCDESIGN-AS. The evolutionary algorithm allows the search for the best parameters and topology of the recurrent neural network to be performed simultaneously. In addition, RCDESIGN-AS has the important characteristic of requiring little computational effort and processing time since it is not necessary for the eigenvalues of the reservoir weight matrix to be reduced and also due to the fact that the augmented states make it possible to reduce the number of neurons in the reservoir. The method was applied for wind speed forecasting with a 24-h ahead horizon using real data of wind speed from five cities in the Northeast Region of Brazil. All results obtained with the proposed method overcame forecasting performed by the persistence method, obtaining prediction gains ranging from 60% to 80% in relation to this reference method. In some datasets, the proposed method also yielded better results than the traditional ESN, showing that RCDESIGN-AS can be a powerful tool for wind-speed forecasting and possibly for other types of time series.

Short-term wind speed forecasting bias correction in the Hangzhou area of China based on a machine learning model

Accurate wind speed forecasting is of great societal importance. In this study, the short-term wind speed forecasting bias at automatic meteorological stations in Hangzhou, Zhejiang Province, China, was corrected using an XGBoost machine learning model called WSFBC-XGB. The products of the local NWP (numerical weather prediction) system were used as the inputs of WSFBC-XGB. The WSFBC-XGB-corrected results were compared with those corrected using the traditional MOS (model output statistics) method. Results showed that WSFBC-XGB performed better than MOS, with the root-mean-square errors (RMSEs)/accuracy rates of the wind speed forecasting (ACCs) of WSFBC-XGB being reduced/ promoted by 26.1% and 7.64%/35.6% and 7.02% relative to NWP and MOS, respectively. The RMSEs/ACCs of WSFBC-XGB were smaller/higher than those of MOS at 90% stations. In addition, the mean decrease in impurity method was used to analyze the interpretability of WSFBC-XGB to help users gain trust in the model. Results showed that the four most important features were the wind speed at 10 m (47.35%), meridional component of wind at 10 m (12.73%), diurnal cycle (9.97%), and meridional component of wind at 1000 hPa (7.45%). The WSFBC-XGB model will help improve the accuracy of short-term wind speed forecasting and provide support for large-scale outdoor activities.摘要准确的风速预报具有重要的社会意义. 在本研究中, 使用名为WSFBC-XGB的XGBoost机器学习模型对中国浙江省杭州市自动气象站的短期风速预报误差进行校正. WSFBC-XGB使用本地数值天气预报系统的产品作为输入. 将WSFBC-XGB校正的结果与传统MOS(模型输出统计)方法校正的结果进行了比较. 结果表明: WSFBC-XGB预报风速的均方根误差(RMSE)/准确率(ACC)分别比NWP和MOS降低/提高了26.1%和7.64%/35.6%和7.02%; 对于90%的站点WSFBC-XGB的RMSE/ACC均小于/高于MOS. 此外, 采用平均杂质减少法对WSFBC-XGB的可解释性进行分析, 以帮助用户增加对模型的信任. 结果表明: 10米风速(47.35%), 10米风的经向分量(12.73%), 日循环(9.97%)和1000百帕风的经向分量(7.45%)是前4个最重要的特征. WSFBC-XGB模型将有助于提高短期风速预报的准确性, 为大型户外活动提供支持.

A Hybrid Neural Network Model for Short-Term Wind Speed Forecasting

This study proposes an effective wind speed forecasting model combining a data processing strategy, neural network predictor, and parameter optimization method. (a) Variational mode decomposition (VMD) is adopted to decompose the wind speed data into multiple subseries where each subseries contains unique local characteristics, and all the subseries are converted into two-dimensional samples. (b) A gated recurrent unit (GRU) is sequentially modeled based on the obtained samples and makes the predictions for future wind speed. (c) The grid search with rolling cross-validation (GSRCV) is designed to simultaneously optimize the key parameters of VMD and GRU. To evaluate the effectiveness of the proposed VMD-GRU-GSRCV model, comparative experiments based on hourly wind speed data collected from the National Renewable Energy Laboratory are implemented. Numerical results show that the root mean square error, mean absolute error, mean absolute percentage error, and symmetric mean absolute percentage error of this proposed model reach 0.2047, 0.1435, 3.77%, and 3.74%, respectively, which outperform the benchmark predictions using popular parameter optimization methods, data processing techniques, and hybrid neural network forecasting models.