Search results

1 – 10 of 345
Article
Publication date: 19 January 2021

Franck Armel Talla Konchou, Pascalin Tiam Kapen, Steve Brice Kenfack Magnissob, Mohamadou Youssoufa and René Tchinda

This paper aims to investigate the profile of the wind speed of a Cameroonian city for the very first time, as there is a growing trend for new wind energy installations…

Abstract

Purpose

This paper aims to investigate the profile of the wind speed of a Cameroonian city for the very first time, as there is a growing trend for new wind energy installations in the West region of Cameroon. Two well-known artificial neural networks, namely, multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX), were used to model the wind speed profile of the city of Bapouh in the West-region of Cameroon.

Design/methodology/approach

In this work, the profile of the wind speed of a Cameroonian city was investigated for the very first time since there is a growing trend for new wind energy installations in the West region of Cameroon. Two well-known artificial neural networks namely multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX) were used to model the wind speed profile of the city of Bapouh in the West-region of Cameroon. The meteorological data were collected every 10 min, at a height of 50 m from the NASA website over a period of two months from December 1, 2016 to January 31, 2017. The performance of the model was evaluated using some well-known statistical tools, such as root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The input variables of the model were the mean wind speed, wind direction, maximum pressure, maximum temperature, time and relative humidity. The maximum wind speed was used as the output of the network. For optimal prediction, the influence of meteorological variables was investigated. The hyperbolic tangent sigmoid (Tansig) and linear (Purelin) were used as activation functions, and it was shown that the combination of wind direction, maximum pressure, maximum relative humidity and time as input variables is the best combination.

Findings

Maximum pressure, maximum relative humidity and time as input variables is the best combination. The correlation between MLP and NARX was computed. It was found that the MLP has the highest correlation when compared to NARX.

Originality/value

Two well-known artificial neural networks namely multi-layer perceptron (MLP) and nonlinear autoregressive network with exogenous inputs (NARX) were used to model the wind speed profile.

Article
Publication date: 30 November 2021

Oluwafemi Ajayi and Reolyn Heymann

Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly…

Abstract

Purpose

Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly increasing global demand for cloud services and other technological services. This projected sectoral growth is expected to translate into increased energy demand from the sector, which is already considered a major energy consumer unless innovative steps are used to drive effective energy management systems. The purpose of this study is to provide insights into the expected energy demand of the DC and the impact each measured parameter has on the building's energy demand profile. This serves as a basis for the design of an effective energy management system.

Design/methodology/approach

This study proposes novel tunicate swarm algorithm (TSA) for training an artificial neural network model used for predicting the energy demand of a DC. The objective is to find the optimal weights and biases of the model while avoiding commonly faced challenges when using the backpropagation algorithm. The model implementation is based on historical energy consumption data of an anonymous DC operator in Cape Town, South Africa. The data set provided consists of variables such as ambient temperature, ambient relative humidity, chiller output temperature and computer room air conditioning air supply temperature, which serve as inputs to the neural network that is designed to predict the DC’s hourly energy consumption for July 2020. Upon preprocessing of the data set, total sample number for each represented variable was 464. The 80:20 splitting ratio was used to divide the data set into training and testing set respectively, making 452 samples for the training set and 112 samples for the testing set. A weights-based approach has also been used to analyze the relative impact of the model’s input parameters on the DC’s energy demand pattern.

Findings

The performance of the proposed model has been compared with those of neural network models trained using state of the art algorithms such as moth flame optimization, whale optimization algorithm and ant lion optimizer. From analysis, it was found that the proposed TSA outperformed the other methods in training the model based on their mean squared error, root mean squared error, mean absolute error, mean absolute percentage error and prediction accuracy. Analyzing the relative percentage contribution of the model's input parameters based on the weights of the neural network also shows that the ambient temperature of the DC has the highest impact on the building’s energy demand pattern.

Research limitations/implications

The proposed novel model can be applied to solving other complex engineering problems such as regression and classification. The methodology for optimizing the multi-layered perceptron neural network can also be further applied to other forms of neural networks for improved performance.

Practical implications

Based on the forecasted energy demand of the DC and an understanding of how the input parameters impact the building's energy demand pattern, neural networks can be deployed to optimize the cooling systems of the DC for reduced energy cost.

Originality/value

The use of TSA for optimizing the weights and biases of a neural network is a novel study. The application context of this study which is DCs is quite untapped in the literature, leaving many gaps for further research. The proposed prediction model can be further applied to other regression tasks and classification tasks. Another contribution of this study is the analysis of the neural network's input parameters, which provides insight into the level to which each parameter influences the DC’s energy demand profile.

Details

Journal of Engineering, Design and Technology , vol. 20 no. 5
Type: Research Article
ISSN: 1726-0531

Keywords

Article
Publication date: 30 June 2020

Sajad Ahmad Rather and P. Shanthi Bala

In this paper, a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA) has…

Abstract

Purpose

In this paper, a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA) has been employed for training MLP to overcome sensitivity to initialization, premature convergence, and stagnation in local optima problems of MLP.

Design/methodology/approach

In this study, the exploration of the search space is carried out by gravitational search algorithm (GSA) and optimization of candidate solutions, i.e. exploitation is performed by particle swarm optimization (PSO). For training the multi-layer perceptron (MLP), CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error. Secondly, a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.

Findings

The experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems. Besides, it gives the best results for breast cancer, heart, sine function and sigmoid function datasets as compared to other participating algorithms. Moreover, CPSOGSA also provides very competitive results for other datasets.

Originality/value

The CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP. Basically, CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power. In the research literature, a little work is available where CPSO and GSA have been utilized for training MLP. The only related research paper was given by Mirjalili et al., in 2012. They have used standard PSO and GSA for training simple FNNs. However, the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms. In this paper, eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs. In addition, a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5% significance level to statistically validate the simulation results. Besides, eight state-of-the-art meta-heuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 5 November 2021

Abdelkader Azzeddine Laouid, Abdelkrim Mohrem and Aicha Djalab

This paper aims to find the minimum possible number of phasor measurement units (PMUs) to achieve maximum and complete observability of the power system and improve the…

Abstract

Purpose

This paper aims to find the minimum possible number of phasor measurement units (PMUs) to achieve maximum and complete observability of the power system and improve the redundancy of measurements, in normal cases (with and without zero injection bus [ZIB]), and then in conditions of a single PMU failure and outage of a single line.

Design/methodology/approach

An efficient approach operates adequately and provides the optimal solutions for the PMUs placement problem. The finest function of optimal PMUs placement (OPP) should be mathematically devised as a problem, and via that, the aim of the OPP problem is to identify the buses of the power system to place the PMU devices to ensure full observability of the system. In this paper, the grey wolf optimizer (GWO) is used for training multi-layer perceptrons (MLPs), which is known as Grey Wolf Optimizer (GWO) based Neural Network (“GW-NN”) to place the PMUs in power grids optimally.

Findings

Following extensive simulation tests with MATLAB/Simulink, the results obtained for the placement of PMUs provide system measurements with less or at most the same number of PMUs, but with a greater degree of observability than other approaches.

Practical implications

The efficiency of the suggested method is tested on the IEEE 14-bus, 24-bus, New England 39-bus and Algerian 114-bus systems.

Originality/value

This paper proposes a new method for placing PMUs in the power grids as a multi-objective to reduce the cost and improve the observability of these grids in normal and faulty cases.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 41 no. 1
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 27 November 2020

Samia Chebira, Noureddine Bourmada, Abdelali Boughaba and Mebarek Djebabra

The increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part…

Abstract

Purpose

The increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs.

Design/methodology/approach

The starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system.

Findings

The performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE, MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system.

Originality/value

The performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.

Details

International Journal of Quality & Reliability Management, vol. 38 no. 6
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 18 June 2020

Mervin Joe Thomas, Mithun M. Sanjeev, A.P. Sudheer and Joy M.L.

This paper aims to use different machine learning (ML) algorithms for the prediction of inverse kinematic solutions in parallel manipulators (PMs) to overcome the…

Abstract

Purpose

This paper aims to use different machine learning (ML) algorithms for the prediction of inverse kinematic solutions in parallel manipulators (PMs) to overcome the computational difficulties and approximations involved with the analytical methods. The results obtained from the ML algorithms and the Denavit–Hartenberg (DH) approach are compared with the experimental results to evaluate their performances. The study is performed on a novel 6-degree of freedom (DoF) PM that offers precise motions with a large workspace for the end effector.

Design/methodology/approach

The kinematic model for the proposed 3-PPSS PM is obtained using the modified DH approach and its inverse kinematic solutions are determined using the Levenberg–Marquardt algorithm. Various prediction algorithms such as the multiple linear regression, multi-variate polynomial regression, support vector, decision tree, random forest regression and multi-layer perceptron networks are applied to predict the inverse kinematic solutions for the manipulator. The data set required to train the network is generated experimentally by recording the poses of the end effector for different instantaneous positions of the slider using the concept of ArUco markers.

Findings

This paper fully demonstrates the possibility to use artificial intelligence for the prediction of inverse kinematic solutions especially for complex geometries.

Originality/value

As the analytical models derived from the geometrical method, Screw theory or numerical techniques involve approximations and needs more computational power, it is not advisable for real-time control of the manipulator. In addition, the data set obtained from the derived inverse kinematic equations to train the network may lead to inaccuracies in the predicted results. This error may generate significant deviations in the end-effector position from the desired position. The present work attempts to resolve this issue by proposing a camera-based approach that uses ArUco library and ML algorithms to create the data set experimentally and predict the inverse kinematic solutions accurately.

Details

Industrial Robot: the international journal of robotics research and application, vol. 47 no. 5
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 30 September 2014

Kabir Bala, Shehu Ahmad Bustani and Baba Shehu Waziri

The purpose of this study was develop a computer-based cost prediction model for institutional building projects in Nigeria through the use of artificial neural network

Abstract

Purpose

The purpose of this study was develop a computer-based cost prediction model for institutional building projects in Nigeria through the use of artificial neural network (ANN) technique. The back-propagation network learns by example and provides good prediction to novel cases.

Design/methodology/approach

The input variables were derived from related works with modification and advices from professionals through a field survey. Two hundred and sixty completed project data were used for training and development of the ANN model. Back-propagation algorithm using the gradient descent delta learning rule with a learning coefficient of 0.4 was used. The input layer of the model comprised of nine variables; building height, compactness of building, construction duration, external wall area, gross floor area, number of floors, proportion of opening on external walls, location index and time index.

Findings

Several multi-layer perceptron networks were developed with varying architecture from which the network 9-7-5-1 was selected. The performance of the model over the validation sample revealed that the model has a mean absolute per cent error of 5.4 per cent and average error of prediction of −2.5 per cent over the sample. The ANN model was considered to be effective for construction cost prediction.

Research limitations/implications

The model may not be suitable for other building types because of the uniqueness of such facility even though significant difference is not anticipated for buildings such as commercial and residential. The models were evaluated based on the prediction errors; other means of evaluation were not used.

Originality/value

The study thus provides a simple, yet effective means of predicting construction costs of institutional building projects in Nigeria using an ANN model.

Details

Journal of Engineering, Design and Technology, vol. 12 no. 4
Type: Research Article
ISSN: 1726-0531

Keywords

Article
Publication date: 9 November 2012

Soheil Ganjefar and Mojtaba Alizadeh

The power system is complex multi‐component dynamic system with many operational levels made up of a wide range of energy sources with many interaction points. Low…

Abstract

Purpose

The power system is complex multi‐component dynamic system with many operational levels made up of a wide range of energy sources with many interaction points. Low frequency oscillations are observed when large power systems are interconnected by relatively weak tie lines. These oscillations may sustain and grow to cause system separation if no adequate damping is available. The present paper aims to propose an on‐line self‐learning PID (OLSL‐PID) controller in order to damp the low frequency power system oscillations in a single‐machine system.

Design/methodology/approach

The proposed OLSL‐PID is used as a controller in order to damp the low frequency power system oscillations. It has a local nature because of its powerful adaption process based on back‐propagation (BP) algorithm that is implemented through an adaptive self‐recurrent wavelet neural network identifier (ASRWNNI). In fact PID controller parameters are updated in on‐line mode, using BP algorithm based on the information provided by the ASRWNNI which is a powerful fast‐acting identifier because of its local nature, self‐recurrent structure and stable training algorithm with ALRs based on discrete lyapunov stability theorem.

Findings

The proposed control scheme is applied to a single machine infinite bus power system under different operating conditions and disturbances. The nonlinear time‐domain simulation results are promising and show the effectiveness and robustness of the proposed controller and also reveal that: because of the high adaptability, the local behavior and high flexibility of the OLSL‐PID controller, it can be damp the low frequency oscillations in the best possible manner and significantly improves the stability performance of the system.

Originality/value

The proposed controller adaption process is done in each sampling period using a powerful adaption law based on BP algorithm. Also during the process the system sensitivity is provided by a powerful fast‐acting identifier. As an alternative to multi‐layer perceptron neural network, self‐recurrent wavelet neural networks (SRWNNs) which combine the properties such as attractor dynamics of recurrent neural network and the fast convergence of the wavelet neural network were proposed to identify synchronous generator. Also to help the OLSL‐PID stability first, PID parameters tuning problem under a wide range of operating conditions is converted to an optimization problem which solved by a chaotic optimization algorithm (COA), and afterwards PID controller is hooked up in the system and on‐line tuning is done in each sampling period.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 31 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 28 March 2008

M. Mostafa

This study uses intelligent modeling techniques with the purpose of examining the effect of various demographic, cognitive and psychographic factors on organ donation in Egypt.

1156

Abstract

Purpose

This study uses intelligent modeling techniques with the purpose of examining the effect of various demographic, cognitive and psychographic factors on organ donation in Egypt.

Design/methodology/approach

Two artificial neural network models (multi‐layer perceptron neural network and probabilistic neural network) are compared to two standard statistical methods (linear discriminant analysis and logistic regression). The variable sets considered are sex, age, educational level, religion, altruistic values, perceived benefits/risks of organ donation, organ donation knowledge, attitudes toward organ donation, and intention to donate organs.

Findings

The results show that artificial neural networks outperform traditional statistical techniques in profiling potential organ donors due to their robustness and flexibility of modeling algorithms.

Originality/value

The paper shows how it is possible to identify various dimensions of organ donation behavior by uncovering patterns in the dataset, and also shows the classification abilities of two neural network techniques.

Details

Marketing Intelligence & Planning, vol. 26 no. 2
Type: Research Article
ISSN: 0263-4503

Keywords

Article
Publication date: 21 May 2021

Saddam Bensaoucha, Youcef Brik, Sandrine Moreau, Sid Ahmed Bessedik and Aissa Ameur

This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector…

282

Abstract

Purpose

This paper provides an effective study to detect and locate the inter-turn short-circuit faults (ITSC) in a three-phase induction motor (IM) using the support vector machine (SVM). The characteristics extracted from the analysis of the phase shifts between the stator currents and their corresponding voltages are used as inputs to train the SVM. The latter automatically decides on the IM state, either a healthy motor or a short-circuit fault on one of its three phases.

Design/methodology/approach

To evaluate the performance of the SVM, three supervised algorithms of machine learning, namely, multi-layer perceptron neural networks (MLPNNs), radial basis function neural networks (RBFNNs) and extreme learning machine (ELM) are used along with the SVM in this study. Thus, all classifiers (SVM, MLPNN, RBFNN and ELM) are tested and the results are compared with the same data set.

Findings

The obtained results showed that the SVM outperforms MLPNN, RBFNNs and ELM to diagnose the health status of the IM. Especially, this technique (SVM) provides an excellent performance because it is able to detect a fault of two short-circuited turns (early detection) when the IM is operating under a low load.

Originality/value

The original of this work is to use the SVM algorithm based on the phase shift between the stator currents and their voltages as inputs to detect and locate the ITSC fault.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 40 no. 3
Type: Research Article
ISSN: 0332-1649

Keywords

1 – 10 of 345