Electricity Price Forecasting in Deregulated Power Markets using Wavelet-ANFIS-KHA

Chandram, K.; Rajababu, Durgam; Raghuram, K.

doi:10.1109/gucon.2018.8674980

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…An application concerning electrical energy price prediction based on both wavelets and ANFIS was presented in [19]. Following the same line as previous works mentioned in this article, the technique provided consistent results in terms of prediction even considering the nonlinear characteristic of the data set.…”

Section: Introductionsupporting

confidence: 62%

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Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System

et al. 2020

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The surface contamination of electrical insulators can increase the electrical conductivity of these components, which may lead to faults in the electrical power system. During inspections, ultrasound equipment is employed to detect defective insulators or those that may cause failures within a certain period. Assuming that the signal collected by the ultrasound device can be processed and used for both the detection of defective insulators and prediction of failures, this study starts by presenting an experimental procedure considering a contaminated insulator removed from the distribution line for data acquisition. Based on the obtained data set, an offline time series forecasting approach with an Adaptive Neuro-Fuzzy Inference System (ANFIS) was conducted. To improve the time series forecasting performance and to reduce the noise, Wavelet Packets Transform (WPT) was associated to the ANFIS model. Once the ANFIS model associated with WPT has distinct parameters to be adjusted, a complete evaluation concerning different model configurations was conducted. In this case, three inference system structures were evaluated: grid partition, fuzzy c-means clustering, and subtractive clustering. A performance analysis focusing on computational effort and the coefficient of determination provided additional parameter configurations for the model. Taking into account both parametrical and statistical analysis, the Wavelet Neuro-Fuzzy System with fuzzy c-means showed that it is possible to achieve impressive accuracy, even when compared to classical approaches, in the prediction of electrical insulators conditions.

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Section: Introductionsupporting

confidence: 62%

“…In the works reported in [5,19,20], ANFIS was assumed for time series forecasting. Fu, Cheng, Yang, and Batista showed in [20] that ANFIS provided better prediction when compared to classical approaches.…”

Section: State-of-the-art Approaches and Comparisonsmentioning

confidence: 99%

Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System

et al. 2020

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“…Trained RNN model can predict P(h) based on input (X) features using Equations ( 1) and (2). Performance of the all these RNN models have been observed in terms of error metrics like Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) [27] as shown in Equations ( 3)-( 5), respectively.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks

et al. 2022

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Electrical load forecasting study is required in electric power systems for different applications with respect to the specific time horizon, such as optimal operations, grid stability, Demand Side Management (DSM) and long-term strategic planning. In this context, machine learning and data analytics models represent a valuable tool to cope with the intrinsic complexity and especially design future demand-side advanced services. The main novelty in this paper is that the combination of a Recurrent Neural Network (RNN) and Principal Component Analysis (PCA) techniques is proposed to improve the forecasting capability of the hourly load on an electric power substation. A historical dataset of measured loads related to a 33/11 kV MV substation is considered in India as a case study, in order to properly validate the designed method. Based on the presented numerical results, the proposed approach proved itself to accurately predict loads with a reduced dimensionality of input data, thus minimizing the overall computational effort.

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“…A Back Propagation Algorithm (BPA) is used to train the ANN because of its flexibility and, learning capability, and is highly suitable for problems with no relationship between output and input [23]. The efficiency of the ANN is measured in terms of MAPE and RMSE [24][25][26]. A stochastic gradient descent approach is used to train the model, and the training dataset consists of 6644 samples and the testing dataset consists of 24 samples.…”

Section: Load Forecastingmentioning

confidence: 99%

Shapley value cooperative game theory-based locational marginal price computation for loss and emission reduction

Veeramsetty

2021

Prot Control Mod Power Syst

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An iterative method based on Shapley Value Cooperative Game Theory is proposed for the calculation of local marginal price (LMP) for each Distributed Generator (DG) bus on a network. The LMP value is determined for each DG on the basis of its contribution to reduce loss and emission reduction, which is assessed using the Shapley Value approach. The proposed approach enables the Distribution Company (DISCO) decision-maker to operate the network optimally in terms of loss and emission. The proposed method is implemented in the Taiwan Power Company distribution network 7 warnings consisting of 84 buses and 11 feeders in the MATLAB environment. The results show that the proposed approach allows DISCO to operate the network on the basis of its priority between the reduction of active power loss and emission in the network

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Electricity Price Forecasting in Deregulated Power Markets using Wavelet-ANFIS-KHA

Cited by 7 publications

References 14 publications

Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System

Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System

Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks

Shapley value cooperative game theory-based locational marginal price computation for loss and emission reduction

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