Wavelet LSTM for Fault Forecasting in Electrical Power Grids

Branco, Nathielle Waldrigues; Cavalca, Mariana Santos Matos; Stefenon, Stéfano Frizzo; Leithardt, Valderi Reis Quietinho

doi:10.3390/s22218323

Cited by 40 publications

(18 citation statements)

References 88 publications

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“…Hybrid methods utilizing LSTM are widely implemented for time series forecasting problems, such as stock prediction [41], which results in improved prediction accuracy [42]. Hybrid versions of LSTM, such as wavelet LSTM, are better in time series prediction compared to the traditional methods used [43]. Trending models for enhanced time series forecasting were proposed in the electrical domain where researchers concluded that a wavelet adaptive neuro-fuzzy inference system outperformed other competent models such as the group method of data handling, LSTM, bootstrap aggregation, sequential learning, and many ensemble learning methods [44].…”

Section: Modern Methods Used For Forecastingmentioning

confidence: 99%

Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5

Narkhede

Hiwale²,

Tidke³

et al. 2023

Algorithms

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Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world’s urban population breathes polluted air. In such a trend of urbanization, predicting the concentrations of pollutants in advance is very important. Predictions of pollutants would help city administrations to take timely measures for ensuring Sustainable Development Goal 11. In data engineering, imputation and the removal of outliers are very important steps prior to forecasting the concentration of air pollutants. For pollution and meteorological data, missing values and outliers are critical problems that need to be addressed. This paper proposes a novel method called multiple iterative imputation using autoencoder-based long short-term memory (MIA-LSTM) which uses iterative imputation using an extra tree regressor as an estimator for the missing values in multivariate data followed by an LSTM autoencoder for the detection and removal of outliers present in the dataset. The preprocessed data were given to a multivariate LSTM for forecasting PM2.5 concentration. This paper also presents the effect of removing outliers and missing values from the dataset as well as the effect of imputing missing values in the process of forecasting the concentrations of air pollutants. The proposed method provides better results for forecasting with a root mean square error (RMSE) value of 9.8883. The obtained results were compared with the traditional gated recurrent unit (GRU), 1D convolutional neural network (CNN), and long short-term memory (LSTM) approaches for a dataset of the Aotizhonhxin area of Beijing in China. Similar results were observed for another two locations in China and one location in India. The results obtained show that imputation and outlier/anomaly removal improve the accuracy of air pollution forecasting.

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Section: Modern Methods Used For Forecastingmentioning

confidence: 99%

Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5

Narkhede

Hiwale²,

Tidke³

et al. 2023

Algorithms

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“…The hybrid model improved the forecast accuracy by reducing the average absolute percentage error rate when compared to the base model [20]. The use of filters for noise reduction can improve the model's ability to make predictions, and besides seasonal filters, the wavelet transform shows promise for this purpose [21], and can be combined with several state-of-the-art models [22], or classical methods such as neuro-fuzzy systems [23].…”

Section: Related Workmentioning

confidence: 99%

Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico

et al. 2023

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The energy price influences the interest in investment, which leads to economic development. An estimate of the future energy price can support the planning of industrial expansions and provide information to avoid times of recession. This paper evaluates adaptive boosting (AdaBoost), bootstrap aggregation (bagging), gradient boosting, histogram-based gradient boosting, and random forest ensemble learning models for forecasting energy prices in Latin America, especially in a case study about Mexico. Seasonal decomposition of the time series is used to reduce unrepresentative variations. The Optuna using tree-structured Parzen estimator, optimizes the structure of the ensembles through a voter by combining several ensemble frameworks; thus an optimized hybrid ensemble learning method is proposed. The results show that the proposed method has a higher performance than the state-of-the-art ensemble learning methods, with a mean squared error of 3.37 × 10−9 in the testing phase.

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“…Classical models can also be employed in this context, like those based on neuro-fuzzy systems [26,27], group method of data handling [28], and multilayer Perceptron [29]. In addition to prediction these approaches can be used for classification [30], through shallow layer structures [31][32][33] or deep learning strategies [34][35][36][37].…”

Section: Time Series Forecastingmentioning

confidence: 99%

LSTM Applied for Commodity Futures Price Forecast

Jesus¹,

Hoppe²,

Sartori³

et al. 2022

Preprint

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This article presents the implementation of a model to estimate the future price of commodities in the Brazilian market from time series of short-term technical evaluation. For this, data from two databases were used, one referring to the foreign market (opening values, maximum, minimum, closing, closing adjustment and volume) and the other, from the Brazilian market (the price of the day), considering commodities, sugar, cotton, corn, soybean and wheat. Subsequently, the technical indicators were calculated from the TA-Lib technical analysis library. Pearson’s correlation coefficient was applied, records with low correlation were removed, and then the database was consolidated. From the pre-processed data, Long Short-Term Memory (LSTM) recurrent neural networks were used to perform data prediction at the one and three day interval. These models were evaluated using the mean square error (MSE), obtaining results between 0.00010 and 0.00037 on test data one day ahead, and from 0.00017 to 0.00042 three days ahead. However, based on the results obtained, it was observed that the developed model obtained a promising forecasting performance for all the commodities evaluated. As a main contribution, there is the consolidation of databases that can be used in future scientific research. Furthermore, based on its interpretation, it can assist in decision making regarding the buying and selling of commodities to increase financial gains.

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Wavelet LSTM for Fault Forecasting in Electrical Power Grids

Cited by 40 publications

References 88 publications

Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5

Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5

Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico

LSTM Applied for Commodity Futures Price Forecast

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