Wavelet group method of data handling for fault prediction in electrical power insulators

Stefenon, Stéfano Frizzo; Ribeiro, Matheus Henrique Dal Molin; Nied, Ademir; Mariani, Viviana Cocco; Coelho, Leandro dos Santos; Rocha, Diovana Fátima Menegat da; Grebogi, Rafael Bartnik; Ruano, A. E.

doi:10.1016/j.ijepes.2020.106269

Cited by 78 publications

(34 citation statements)

References 38 publications

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“…In addition to electricity price and load forecasting, the use of artificial intelligence is powerful to assess the development of possible failures in the electrical system [29]. As presented by Stefenon et al [30], the use of the wavelet transform reduces signal noise, improving the analysis of chaotic time signals. The results using the wavelet group method of data handling proved to be superior to well-consolidated algorithms as LSTM and adaptive neuro fuzzy inference system.…”

Section: Related Workmentioning

confidence: 99%

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

et al. 2020

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Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

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Section: Related Workmentioning

confidence: 99%

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

et al. 2020

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“…As the number of decompositions of the binary tree emitted from the orthogonal wavelet can be very large, it is interesting to find an ideal decomposition in relation to a convenient criterion, computable by an efficient algorithm to find a minimum criterion. From the optimum binary wavelet packet tree, a signal with less noise is obtained [31].…”

Section: Deep Learning For Time‐series Forecastingmentioning

confidence: 99%

Hybrid deep learning for power generation forecasting in active solar trackers

Stefenon

Kasburg²,

Nied

et al. 2020

IET Generation, Transmission & Distribution

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“…. , 9). Note that the model was estimated, taking the main hierarchical adjustment approaches into account, for the following levels: (i) total power generation in Brazil (Level 0), (ii) total energy generation by electrical subsystem (Level 1), and (iii) total energy generation by the energy generating source (Level 2).…”

Section: Evaluating Forecast Accuracymentioning

confidence: 99%

“…In relation to the object of our study, power generation, there are several forecasting applications: (i) classical time series models like the autoregressive moving average, autoregressive integrated moving average, and generalized autoregressive conditional heteroscedastic among others [7,8]; (ii) pre-processing techniques like spectrum analysis, wavelets, and Fourier analysis [9]; and, (iii) machine learning approaches such as neural networks, fuzzy systems, and support vector machine [10]. Alternatively, hybrid models aim to combine machine learning representations with different methods.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Hierarchical Time Series in Power Generation

Gontijo

Costa

2020

Energies

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Academic attention is being paid to the study of hierarchical time series. Especially in the electrical sector, there are several applications in which information can be organized into a hierarchical structure. The present study analyzed hourly power generation in Brazil (2018–2020), grouped according to each of the electrical subsystems and their respective sources of generating energy. The objective was to calculate the accuracy of the main measures of aggregating and disaggregating the forecasts of the Autoregressive Integrated Moving Average (ARIMA) and Error, Trend, Seasonal (ETS) models. Specifically, the following hierarchical approaches were analyzed: (i) bottom-up (BU), (ii) top-down (TD), and (iii) optimal reconciliation. The optimal reconciliation models showed the best mean performance, considering the primary predictive windows. It was also found that energy forecasts in the South subsystem presented greater inaccuracy compared to the others, which signals the need for individualized models for this subsystem.

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Wavelet group method of data handling for fault prediction in electrical power insulators

Cited by 78 publications

References 38 publications

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

Hybrid deep learning for power generation forecasting in active solar trackers

Forecasting Hierarchical Time Series in Power Generation

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