A Neuro-fuzzy Approach for Predicting Load Peak Profile

Abdellah, Draidi; Djamel, Labed

doi:10.11591/ijece.v5i6.pp1304-1310

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…Neural networks (NNs) estimated the flow pattern Bottom Hole Pressure with less than 5% error and frictional pressure drop with less than 30%. Satisfactory results have been found for three phase relative permeability compared with experiments using adopted a PSO and neurofuzzy models to train the perceptron and to predict pollutant levels in gas wells [17]- [19]. The approach was proved to be feasible and effective by applying to some real air-quality problems and by comparison with a simple back-propagation (BP) algorithm.…”

Section: Introductionmentioning

confidence: 93%

Neural Networks for Flow Bottom Hole Pressure Prediction

Awadalla¹,

Yousef

2016

IJECE

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Installation of down-hole gauges in oil wells to determine Flowing Bottom-Hole Pressure (FBHP) is a dominant process especially in wells lifted with electrical submersible pumps. However, intervening a well occasionally is an exhaustive task, associated with production risk, and interruption. The previous empirical correlations and mechanistic models failed to provide a satisfactory and reliable tool for estimating pressure drop in multiphase flowing wells. This paper aims to find the optimum parameters of Feed-Forward Neural Network (FFNN) with back-propagation algorithm to predict the flowing bottom-hole pressure in vertical oil wells. The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and for the sake of qualitative comparison, Radial Basis neural and network and the empirical modes have been developed. The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 12.5% of the total data sets to give 3.4 RMSE and 97% of the test data achieved 90% accuracy.

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Section: Introductionmentioning

confidence: 93%

Neural Networks for Flow Bottom Hole Pressure Prediction

Awadalla¹,

Yousef

2016

IJECE

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“…One relatively new approach to management is the application of a fuzzy controller. Fuzzy controllers are systems that actively regulate dynamic environment [9]. A prototypical example is a temperature controller on the inputs from a temperature sensor.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…A prototypical example is a temperature controller on the inputs from a temperature sensor. It sets the engine temperature control devices to cool or warm environment [8], [9]. The general scheme of a fuzzy controller is illustrated in Figure 3.…”

Section: Fuzzy Logicmentioning

confidence: 99%

“…As with any approximation, ambiguities and uncertainties are possible; hence, errors may occur. However, ANFIS has been used in many engineering applications and especially in distribution and smart networks for modelling electricity demand [8]- [9], fault diagnosis [10] and fault location [11]. Very few papers deal with distribution losses estimation using ANFIS [12].…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks

Mlakić¹,

Nikolovski

Knežević

2016

IJECE

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The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

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“…Some are using conventional methods, such as moving average and exponential smoothing methods [3][4][5][6], and some others are using soft computing methods, such as neural networks and fuzzy inference system [7][8][9]. Moreover, hybrid forecasting methods have also been developed by many researchers as we can find in the works of Zhiyuan et al [10], Draidi and Labed [11], and Popoola et al [12].…”

Section: Introductionmentioning

confidence: 99%

Big 5 ASEAN capital markets forecasting using WEMA method

Hansun¹,

Kristanda²,

Winarno³

2019

TELKOMNIKA

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ASEAN through ASEAN Economics Community (AEC) 2020 treaty has proposed financial integration via capital markets integration in order to aim comprehensive ASEAN economic integration. Therefore, the need to have a proper prediction of ASEAN capital market becomes a major issue. In this study, we took big 5 ASEAN capital markets, i.e. Straits Times Index (STI), Kuala Lumpur Stock Exchange (KLSE), Stock Exchange of Thailand (SET), Jakarta Stock Exchange (JKSE), and Philippine Stock Exchange (PSE) to be forecasted using WEMA method. Weighted Exponential Moving Average (WEMA) is a new hybrid moving average method which combines the weighting factor calculation in Weighted Moving Average (WMA) with the procedure of Exponential Moving Average (EMA). WEMA has successfully been implemented and used to forecaste discrete time series data, but never being used to forecast ASEAN capital markets. In this study, we took further action by implementing the WEMA method with brute force approach for scaling factor tuning on big 5 ASEAN capital markets. From the experimental results, we found that WEMA has successfully forecasted all those exchanges. By looking at the forecast error measurement, it gives the best performance on PSE and worst performance on SET dataset among all datasets being considered in this study.

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A Neuro-fuzzy Approach for Predicting Load Peak Profile

Cited by 7 publications

References 9 publications

Neural Networks for Flow Bottom Hole Pressure Prediction

Neural Networks for Flow Bottom Hole Pressure Prediction

An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks

Big 5 ASEAN capital markets forecasting using WEMA method

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