Wind power forecast based on improved Long Short Term Memory network

In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. VMD is used to decompose landslide displacement into different displacement subsequences, and the SLSTM network is used to forecast each displacement subsequence. Then, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all displacement subsequences. On the other hand, the SLSTM networks are updated by adding the forecast values into the training set, realizing the dynamic displacement forecasting. The proposed model was verified on the Dashuitian landslide in China. The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)–LSTM network, the proposed model has higher forecast accuracy.

Accurate wind power data prediction is crucial to increase wind energy usage since wind power data are characterized by uncertainty and randomness, which present significant obstacles to the scheduling of power grids. This paper proposes a hybrid model for wind power prediction based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), bidirectional long short-term memory network (BiLSTM), and Markov chain (MC). First, CEEMDAN is used to decompose the wind power series into a series of subsequences at various frequencies, and then SE is employed to reconstruct the wind power series subsequences to reduce the model’s complexity. Second, the long short-term memory (LSTM) network is optimized, the BiLSTM neural network prediction method is used to predict each reconstruction component, and the results of the different component predictions are superimposed to acquire the total prediction results. Finally, MC is used to correct the model’s total prediction results to increase the accuracy of the predictions. Experimental validation with measured data from wind farms in a region of Xinjiang, and computational results demonstrate that the proposed model can better fit wind power data than other prediction models and has greater prediction accuracy and generalizability for enhancing wind power prediction performance.

Wind power deviation charge reduction using long short term memory network

A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm

Fossil fuels cause environmental and ecosystem problems. Hence, fossil fuels are replaced by nonpolluting, renewable, and clean energy sources such as wind energy. The stochastic and intermittent nature of wind speed makes it challenging to obtain accurate predictions. Long short term memory (LSTM) networks are proved to be reliable models for time series forecasting. Hence, an improved deep learning-based hybrid framework to forecast wind speed is proposed in this paper. The new framework employs a stacked autoencoder (SAE) and a stacked LSTM network. The stacked autoencoder extracts more profound and abstract features from the original wind speed dataset. Empirical tests are conducted to identify an optimal stacked LSTM network. The extracted features from the SAE are then transferred to the optimal stacked LSTM network for predicting wind speed. The efficiency of the proposed hybrid model is compared with machine learning models such as support vector regression, artificial neural networks, and deep learning based models such as recurrent neural networks and long short term memory networks. Statistical error indicators, namely, mean absolute error, root mean squared error, and R2, are adopted to assess the performance of the models. The simulation results demonstrate that the suggested hybrid model produces more accurate forecasts.

A new financial data forecasting model using genetic algorithm and long short-term memory network

The wind power forecasting (WPF) technology can reduce the adverse impact of wind power grid connection. Based on the characteristics of wind power data, an algorithm based on improved variational mode decomposition (IVMD) and long short-term memory (LSTM) Network is proposed to predict the wind power, and hyper parameter optimization search of LSTM using Whale Swarm Algorithm with Iterative Counter (WSA-IC). Firstly, through correlation analysis, the characteristics of 10 different wind power data are screened, and two kinds of data with large correlation with wind power are determined as input of the mode. Secondly, IVMD is used to calculate the maximum envelope kurtosis, determine the best decomposition parameters of the variational mode decomposition (VMD), and the original wind power and wind speed sequences are decomposed to obtain the IMF with different time scales. Finally, to address the problems of difficult optimization of hyper parameter and difficulty in obtaining optimal solutions for LSTM neural network modes, the WSA-IC algorithm is proposed to optimize its key hyper parameter, and the IVMD-WSA-IC-LSTM forecasting mode is established to obtain the short-term forecasting results of wind power. The algorithm is tested with the data of China Longyuan Power Group Corporation Limited. Compared with other common forecasting approaches using same data, the mean absolute error (MAE) of the forecasting approach is reduced to 0.007859, the mean square error (MSE) is reduced to 0.00011, and the determination coefficient is improved to 0.998828, which has higher forecasting accuracy.

In traditional financial performance evaluation models, parameter settings are often too large or too small, resulting in significant model errors. To address this issue, an improved artificial bee colony algorithm was proposed and applied to optimize the parameters of performance evaluation models. This method first constructs a corporate financial performance evaluation system, and then improves the artificial bee colony algorithm with differential evolution algorithm to optimize the parameters of the long short-term memory network, in order to improve the accuracy of the long short-term memory network in corporate financial performance evaluation. The results showed that the improvement of the ABC algorithm was effective. The improved ABC algorithm converged on the Ackley function in the 800th iteration, and the ABC algorithm converged in the 1400th iteration. The evaluation error of the proposed method is the lowest, with the algorithm having the lowest four errors of -0.0121, 0.0453, 0.0683, and 0.0047, respectively. Among the other algorithms, the comprehensive error of the financial performance evaluation model based on Long Short Term Memory (LSTM) network is relatively low, but still lower than the algorithm proposed in the study. The research proposes a long short-term memory network optimized based on improved artificial bee colony algorithm, which can accurately evaluate the financial performance of enterprises, help them review their own development level, and clarify their future development direction.

With the in-depth implementation of China’s “National Strategy for Building a Strong Transportation Network,” the scale of expressway construction has continued to expand. As a result, the number of high-fill and deep-cut subgrade projects under complex geological conditions has increased significantly, leading to a surge in landslide-related issues. Consequently, accurate prediction of slope displacement is of critical importance for early warning and prevention of landslide disasters. This study proposes a hybrid prediction model, VMD-MPA-LSSVM-LSTM (VMLL), which integrates Variational Mode Decomposition (VMD), Marine Predators Algorithm (MPA), Least Squares Support Vector Machine (LSSVM), and Long Short-Term Memory (LSTM) networks. Using monitoring data from the high-fill embankment slope at Hongtuyao as the research subject, the VMLL model is employed to predict slope displacement based on small-sample data. The objective is to provide a more accurate method for early warning of slope displacement. Firstly, the original monitoring data are decomposed into trend displacement components and fluctuation displacement components using Variational Mode Decomposition (VMD). Subsequently, the trend component and the fluctuation component are predicted using Least Squares Support Vector Machine (LSSVM) and Long Short-Term Memory (LSTM) networks, respectively. Finally, the Marine Predators Algorithm (MPA) is employed to optimize the hyperparameters of the predictive models. Based on this framework, a VMLL-based slope displacement prediction model is constructed. To verify the superiority of the VMLL model, a comparative analysis was conducted against LSSVM, LSTM, and the VMD-LSSVM-LSTM models. The results demonstrate that the VMLL model achieves the highest prediction accuracy, with a Mean Absolute Percentage Error (MAPE) of 0.4022%, a Mean Absolute Error (MAE) of 0.016 mm, a Root Mean Square Error (RMSE) of 0.0206 mm, a coefficient of determination (R²) of 94.08%, and a Variance Accounted For (VAF) of 96.5%. Compared with the other three models, the VMLL model reduces the MAPE, MAE, and RMSE by 30.41–67.62%, 30.38–67.40%, and 27.32–71.00%, respectively. Meanwhile, it improves the R² and VAF by 5.95–217.51% and 0.38–11.48%, respectively. These improvements clearly demonstrate that the VMLL model outperforms the other models, indicating its significant advantage over single prediction models. Furthermore, three additional datasets were used to evaluate the model’s performance. The average values of the performance evaluation metrics for these datasets were 0.6207% (MAPE), 0.0238 mm(MAE), 0.0273 mm(RMSE), 90.33%(R²), and 96.81%(VAF), respectively. These results demonstrate the high accuracy and strong robustness of the proposed model in predicting both horizontal displacement and vertical settlement of slopes, providing a reliable methodological framework for slope stability assessment and landslide disaster early warning.

This paper explains prediction of share market trends of organizations using Artificial Neural Network (ANN). The Long Short Term Memory (LSTM) incorporated with a simple neural network gives the result of the movement of company's stock prices in the share market. LSTM is used for processing the time-series data. LSTM is a type of Recurrent Neural Network (RNN). In this work, layers of LSTM networks called stacked LSTM is a core component that process the huge volume of time series data. LSTM model works like a human brain because of the power to have a short term and long term memory. During data processing in the training stage, the model keeps a short term memory of the relation between the date and stock prices which is available in the data. It then starts keeping track of the relations from the successive dates and stock prices since the inception of the company. In this stage, the model tries to find a pattern or a trend in the stock price movement. This is kept in the long term memory. As the model processes further data, it finds an accurate pattern in the stock price movement. The exact date or a number of days is given as input and the stock price is given as output from the model

Modern decision-making in fixed income asset management benefits from intelligent systems, which involve the use of state-of-the-art machine learning models and appropriate methodologies. We conduct the first study of bond yield forecasting using long short-term memory (LSTM) networks, validating its potential and identifying its memory advantage. Specifically, we model the 10-year bond yield using univariate LSTMs with three input sequences and five forecasting horizons. We compare those with multilayer perceptrons (MLP), univariate and with the most relevant features. To demystify the notion of black box associated with LSTMs, we conduct the first internal study of the model. To this end, we calculate the LSTM signals through time, at selected locations in the memory cell, using sequence-to-sequence architectures, uni and multivariate. We then proceed to explain the states’ signals using exogenous information, for what we develop the LSTM-LagLasso methodology. The results show that the univariate LSTM model with additional memory is capable of achieving similar results as the multivariate MLP using macroeconomic and market information. Furthermore, shorter forecasting horizons require smaller input sequences and vice-versa. The most remarkable property found consistently in the LSTM signals, is the activation/deactivation of units through time, and the specialisation of units by yield range or feature. Those signals are complex but can be explained by exogenous variables. Additionally, some of the relevant features identified via LSTM-LagLasso are not commonly used in forecasting models. In conclusion, our work validates the potential of LSTMs and methodologies for bonds, providing additional tools for financial practitioners.

Background: Cardiac arrhythmias are one of the leading causes of heart failure. In particular, atrial fibrillation (AFib) is a kind of arrhythmia that can lead to heart stroke and myocardial infarction. It is very important and crucial to predict AFib at an early stage to prevent heart disease. Electrocardiogram is one of the premium diagnostic tools which is used by most of the researchers for predicting irregular heartbeats. There are many works carried out in finding heart disease using machine learning classifiers. Aims and Objectives: Deep learning based hybrid Long Short Term Memory (LSTM) network is hybridized with Enhanced Whale Optimization (EWO) to minimize the network optimization and configuration issues faced in the existing models and proposed to increases the accuracy of predicting AFib. Materials and Methods: The proposed LSTM network is hybridized with a EWO technique for predicting AFib. This study uses a hybrid LSTM EWO network for classifying the various output labels of heart disease. EWO is used to predict the most relevant features from the raw dataset. Then, the LSTM model is used to predict the AFib of a patient from normal ECG data. Results: The DL based LSTM EWO achieves better results in all the performance metrics by analyzing the optimized features in feature space, training, and testing phase and successfully obtains better performance in an effective manner. LSTM improves the accuracy by reducing the number of units in the hidden layer which optimizes the network configuration. The proposed model achieves 96.12% accuracy which is 12.81% higher than RF, 15.01% higher than GB, 28.04% higher than CART, and 16.92% higher than SVM. Conclusion: The proposed model hybrid LSTM network integrated EWO for predicting the AFib. The EWO is applied for selecting the most appropriate features needed for the model to learn and produce improvised performance. The optimization and network configuration problems faced in the existing studies are avoided by choosing the suitable number of LSTM units and the size of the time window. This has been implemented through LSTM units and their window size. In addition, we made a statistical examination to prove the importance of proposed work against other models. It is observed that the experimental results attained with 96% of accuracy, better than conventional models.

A hybrid model for carbon price forecasting using GARCH and long short-term memory network

River runoff simulation and prediction are important for controlling the water volume and ensuring the optimal allocation of water resources in river basins. However, the instability of medium- and long-term runoff series increases the difficulty of runoff forecasting work. In order to improve the prediction accuracy, this research establishes a hybrid deep learning model framework based on variational mode decomposition (VMD), the mutual information method (MI), and a long short-term memory network (LSTM), namely, VMD-LSTM. First, the original runoff data are decomposed into a number of intrinsic mode functions (IMFs) using VMD. Then, for each IMF, a long short-term memory (LSTM) network is applied to establish the prediction model, and the MI method is used to determine the data input lag time. Finally, the prediction results of each subsequence are reconstructed to obtain the final forecast result. We explored the predictive performance of the model with regard to monthly runoff in the upper Heihe River Basin, China, and compared its performance with other single and hybrid models. The results show that the proposed model has obvious advantages in terms of the performance of point prediction and interval prediction compared to several comparative models. The Nash–Sutcliffe efficiency coefficient (NSE) of the prediction results reached 0.96, and the coverage of the interval prediction reached 0.967 and 0.908 at 95% and 90% confidence intervals, respectively. Therefore, the proposed model is feasible for simulating the monthly runoff of this watershed.

Hydrate plugging and pipeline leak can impair the normal operation of natural gas pipeline and may lead to serious accidents. Since natural gas pipeline safety monitoring based on active acoustic excitation can detect and locate not only the two abnormal events but also normal components such as valves and pipeline elbows, recognition and classification of these events are of great importance to provide maintenance guidance for the pipeline operators and avoid false alarm. In this article, long short-term memory (LSTM) network is introduced and applied to classify detection signals of hydrate plugging, pipeline leak, and elbow. Adaptive moment estimation (Adam) algorithm is introduced and utilized to accelerate the long short-term memory network convergence in training. Experimental results demonstrate that the network with three layers and 64 units per cell performs the best. The cross-entropy loss in training is 0.0005, and classification accuracies are all 100% in training, validation, and testing which verify the validity of the long short-term memory network. Therefore, the method based on the long short-term memory network and adaptive moment estimation algorithm can work efficiently on pipeline events classification and has great guiding significance for safety assurance of natural gas transmission.