Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks

Alanis, Alma Y.; Sánchez, Oscar D.; Álvarez, Jesús Gerardo Cruz

doi:10.3390/math9101075

Cited by 8 publications

(4 citation statements)

References 31 publications

(60 reference statements)

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“…This paper proposes a system log anomaly detection method based on dual LSTM. Through the cooperation of two LSTM models, the gradient problem of a single LSTM model in long sequence prediction [27] is solved, and the performance of anomaly detection is improved. Figure 2 shows the three main modules of this method: log parsing, log key anomaly detection model, and anomaly detection workflow model.…”

Section: Methodsmentioning

confidence: 99%

LogLS: Research on System Log Anomaly Detection Method Based on Dual LSTM

Chen

Luktarhan

2022

Symmetry

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System logs record the status and important events of the system at different time periods. They are important resources for administrators to understand and manage the system. Detecting anomalies in logs is critical to identifying system faults in time. However, with the increasing size and complexity of today’s software systems, the number of logs has exploded. In many cases, the traditional manual log-checking method becomes impractical and time-consuming. On the other hand, existing automatic log anomaly detection methods are error-prone and often use indices or log templates. In this work, we propose LogLS, a system log anomaly detection method based on dual long short-term memory (LSTM) with symmetric structure, which regarded the system log as a natural-language sequence and modeled the log according to the preorder relationship and postorder relationship. LogLS is optimized based on the DeepLog method to solve the problem of poor prediction performance of LSTM on long sequences. By providing a feedback mechanism, it implements the prediction of logs that do not appear. To evaluate LogLS, we conducted experiments on two real datasets, and the experimental results demonstrate the effectiveness of our proposed method in log anomaly detection.

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Section: Methodsmentioning

confidence: 99%

LogLS: Research on System Log Anomaly Detection Method Based on Dual LSTM

Chen

Luktarhan

2022

Symmetry

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“…Let the number of inputs and hidden layer nodes be

p

and

m

, respectively.

\begin{matrix} W = [w_{ij}], i = 1, 2, \dots, p; j = 1, 2, \dots, m \\ Θ = [b_{j}], j = 1, 2, \dots, m \\ {italicneth}_{j} = \sum_{i = 1}^{p} w_{italicij} x_{t - i} + b_{i}, j = 1, 2, \dots, m \end{matrix}

The outputs of the hidden layer nodes

((), {oh}_{j}, j = 1, 2, \dots, m)

are calculated by using logistic activation functions by Equation (3):

{oh}_{j} goodbreak= \frac{1}{1 + \exp ((), goodbreak- {neth}_{j})}, j goodbreak= 1, 2, \dots, m

The output of the pi‐sigma part is calculated with (Alanis et al, 2021).

{output}_{t}^{italicpi - italicsigma} goodbreak= \prod_{j = 1}^{m} {oh}_{j}

The output of the exponential smoothing can be calculated with (Balcilar et al, 2022).…”

Section: Proposed Method: a New Recurrent Pi‐sigma Artificial Neural ...mentioning

confidence: 99%

“…Selvanambi et al (2020) proposed a recurrent high‐order neural network for predicting lung cancer. Alanis et al (2021) proposed a recurrent high‐order neural network approach for forecasting wind energy resources.…”

Section: Introductionmentioning

confidence: 99%

A new recurrent pi‐sigma artificial neural network inspired by exponential smoothing feedback mechanism

Baş

Eğrioğlu

2022

Journal of Forecasting

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Pi-sigma artificial neural networks have very good performance for forecasting problems because of their highly nonlinear model structure. Some time series can be forecasted better with the combination of simple and highly nonlinear structures. In this study, the architecture of the pi-sigma artificial neural network is modified by an inspiring exponential smoothing feedback mechanism. The architecture of the proposed neural network automatically balances simple and complex nonlinear model structures. Moreover, a training algorithm is created by using the sine cosine algorithm. The training algorithm has some solutions for overfitting and local optimum problems. The main contribution of the study is to propose a new hybrid recurrent neural network and its training algorithm. In the analysis of the new network's performance, the randomly selected eight subseries of the Financial Times Stock Exchange 100 Index daily time series between 2014 and 2018 are used. The performance of the proposed method is compared with popular deep learning artificial neural networks, pisigma artificial neural networks, and classical exponential smoothing methods. The statistical results show that the proposed method can produce better forecasting results than the others.

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“…It uses artificial neural networks and fuzzy logic systems. The authors in [6] validated the artificial neural network model on an onshore wind farm in Denmark, demonstrating that its mean absolute error is close to 6%. A hybrid approach combining a mode decomposition method and empirical mode decomposition with support vector regression was proposed in [7].…”

Section: Introductionmentioning

confidence: 96%

Wind Power Short-Term Time-Series Prediction Using an Ensemble of Neural Networks

Ciechulski,

Osowski

2024

Energies

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Short-term wind power forecasting has difficult problems due to the very large variety of speeds of the wind, which is a key factor in producing energy. From the point of view of the whole country, an important problem is predicting the total impact of wind power’s contribution to the country’s energy demands for succeeding days. Accordingly, efficient planning of classical power sources may be made for the next day. This paper will investigate this direction of research. Based on historical data, a few neural network predictors will be combined into an ensemble that is responsible for the next day’s wind power generation. The problem is difficult since wind farms are distributed in large regions of the country, where different wind conditions exist. Moreover, the information on wind speed is not available. This paper proposes and compares different structures of an ensemble combined from three neural networks. The best accuracy has been obtained with the application of an MLP combiner. The results of numerical experiments have shown a significant reduction in prediction errors compared to the naïve approach. The improvement in results with this naïve solution is close to two in the one-day-ahead prediction task.

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Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks

Cited by 8 publications

References 31 publications

LogLS: Research on System Log Anomaly Detection Method Based on Dual LSTM

LogLS: Research on System Log Anomaly Detection Method Based on Dual LSTM

A new recurrent pi‐sigma artificial neural network inspired by exponential smoothing feedback mechanism

Wind Power Short-Term Time-Series Prediction Using an Ensemble of Neural Networks

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