Orthogonal Flower Pollination Algorithm based Mixed Kernel Extreme Learning Machine for Analog Fault Prognositcs

Cui, Weijia; He, Yuzhu

doi:10.1109/itaic.2019.8785740

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…To evaluate the accuracy of the models developed with DLs, the criteria of Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Regression (R) are used. The following equations show how to calculate them [38,39]: For this purpose, Equation (34) has been used to normalize the training and test patterns [40].…”

Section: Methodsmentioning

confidence: 99%

“…To evaluate the accuracy of the models developed with DLs, the criteria of Mean Squared Error ( MSE ), Root Mean Square Error ( RMSE ), Mean Absolute Percentage Error ( MAPE ), and Regression ( R ) are used. The following equations show how to calculate them [38, 39]:

\begin{equation} RMSE = \sqrt {\frac{{\mathop \sum \nolimits_{l = 1}^N {{\left( {{Y_l} - {{\hat Y}_l}} \right)}^2}}}{N}} \end{equation}

\begin{equation} MSE = \frac{{\mathop \sum \nolimits_{l = 1}^N {{( {{Y_l} - {{\hat Y}_l}})}^2}}}{N}\end{equation}

\begin{equation} MAPE = \frac{{\mathop \sum \nolimits_{l = 1}^N \left| {\frac{{{Y_l} - {{\hat Y}_l}}}{{{Y_l}}}} \right|}}{N}\end{equation}

\begin{equation} R = output = a^{\prime}.target + b\end{equation}

…”

Section: Methodsmentioning

confidence: 99%

“…Each layer can adjust the number of filters, the kernel size, and the number of strides. Changing the parameters of layers can affect learning speed and performance depending on the characteristics of the learning data [39].…”

Section: Methodsmentioning

confidence: 99%

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Optimal demand response programs selection using CNN‐LSTM algorithm with big data analysis of load curves

Alikhani

Moghaddam

Moazzen

2022

IET Generation Trans & Dist

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One of the problems in implementing DRPs is the lack of sufficient understanding of consumers' behaviour when implementing DRPs. This paper compares consumers' load patterns annually by the improved Weighted Fuzzy Average (WFA) K-means clustering method. According to the collected data, DRPs are discussed annually using a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). To make CNN-LSTM a practical algorithm for executing DRPs, Time Series Prediction (TSP) operations must be performed, and input data must be memorized. Finally, according to the TSP done for the obtained data, the implementation of DRPs in practice is facilitated. Then, the DRPs executed with the Deep Learning (DL) model and the power model will be prioritized by Technique for Order of Preference by Similarity to the Ideal Solution (TOP-SIS) method, and decision indicators will be determined and weighted using the Shannon entropy method. The numerical studies section shows that the CNN-LSTM algorithm is able to simulate DRPs with the Mean Absolute Percentage Error (MAPE) almost below 1% in the residential cluster and the highest MAPE in the commercial cluster of 15%. Nevertheless, this algorithm could easily give the same answer as the DRPs power model in the optimal selection of programs.

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Section: Methodsmentioning

confidence: 99%

“…To evaluate the accuracy of the models developed with DLs, the criteria of Mean Squared Error ( MSE ), Root Mean Square Error ( RMSE ), Mean Absolute Percentage Error ( MAPE ), and Regression ( R ) are used. The following equations show how to calculate them [38, 39]: