M5P model tree based fast fuzzy maximum power point tracker

Blaifi, S.; Moulahoum, Samir; Benkercha, Rabah; Taghezouit, Bilal; Saim, Abdelhakim

doi:10.1016/j.solener.2018.01.071

Cited by 62 publications

(18 citation statements)

References 43 publications

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“…The third step is the defuzzification where the process converts the linguistic decision into numerical output. Figure 12 describes briefly the fuzzy processing steps [39]. For both inputs and output, five trapezoidal and triangular membership functions have been employed namely: NS (negative strong), NL (negative low), ZE (zero), PL (positive low) and PS (positive strong).…”

Section: The Mppt Usedmentioning

confidence: 99%

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Static and Dynamic Photovoltaic Cell/Module Parameters Identification

Blaifi¹,

Taghezouit²

2020

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

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The accurate parameters extraction is an important step to obtain a robust PV outputs forecasting for static or dynamic modes. For these aims, several approaches have been proposed for photovoltaic (PV) cell modeling including electrical circuitbased model, empirical models, and non-parametrical models. Moreover, numerous parameter extraction methods have been introduced in the literature depending on the proposed model and the operating mode. These methods can be classified into two main approaches including automatic numerical and analytical approaches. These approaches are commonly applied in the static mode, whereas they can be employed for dynamic parameters extraction. In this chapter, as a first stage, the static parameters extraction for both single and double diodes models is exposed wherein Genetic Algorithm and outdoor measurements are considered for fixed irradiation and temperature. In the second stage, a dynamic parameters extraction is carried out using Levenberg-Marquardt algorithm, where 1 day profile outdoor measurement is considered. After that, the robustness of the proposed approaches is evaluated and the parameters obtained by the static method and that given by the dynamic technique are compared. The test is carried out using 3 days with different weather conditions profiles. The obtained results show that the parameters extraction by dynamic techniques gives satisfactory performances in terms of agreement with the real data. literature based mainly on several approaches including electrical, empirical, and non-parametrical modeling. For the non-parametrical models, two approaches are introduced namely, Artificial Neural Network (ANN) and Neuron-Fuzzy based models. The accuracy and the robustness of these approaches rely strongly on the richness of the training dataset in terms of scenarios. Neuron-Fuzzy techniques has been used to predict cell short-circuit current and open-circuit voltage for the static representation [1]. The ANN-based technique is introduced to model the PV array power for the embedded systems implementation [2]. This technique has been tested for dynamic mode dealing [3]. The ANN technique is also used for the prediction of the PV cell/module voltage directed to amorphous silicon PV technology wherein the obtained network has been tested by real dynamic data [4].In the empirical or the analytical approaches, several models have been proposed to estimate accurately both static and dynamic modes of PV cell/modules. An analytical model is proposed by [5] based on manufacturer characteristic. This model provides acceptable results for both static and dynamic working. Another model has been introduced by Sandia National Laboratory [6], which is widely employed for PV cell/model forecasting especially for the large-scale arrays. Other empirical models have been proposed in order to estimate the PV array power under uniform shading [7,8].In the electrical approaches, two models widely prevail owing to their simplicity based on equivalent circuits namely: single (SDM) and doub...

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Section: The Mppt Usedmentioning

confidence: 99%

“…The center of gravity based method is used for the defuzzification to provide the control duty cycle after applying the Mamdani inference model given in Table 6. Figure 13a, b and d describes the used membership functions [39].…”

Section: The Mppt Usedmentioning

confidence: 99%

Static and Dynamic Photovoltaic Cell/Module Parameters Identification

Blaifi¹,

Taghezouit²

2020

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

Self Cite

View full text Add to dashboard Cite

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“…where ai is a value of attribute I; and 0 to are weights. To calculate the weights, the least square error algorithm is minimized [58]:…”

Section: Multi-linear Regression Model (Mlr)mentioning

confidence: 99%

“…where A is attribute vector. The initial model obtained from the M5P algorithm has two basic flaws [58]:…”

Section: Multi-linear Regression Model (Mlr)mentioning

confidence: 99%

Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

Karballaeezadeh¹,

Tehrani²,

S.³

et al. 2020

Preprint

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The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: 1. falling weight deflectometer and ground-penetrating radar are expensive tests, 2. back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, m5p model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

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“…Originally, in addition to adding the linear regression method to the tree leave nodes, M5 model is also based on the conventional decision tree. The trained data is used by the algorithm to form the nodes and represent the decision tree model [22]. The ZeroR algorithm is one of the simplest classifiers.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

A Review of Machine Learning Models for Software Cost Estimation

Arslan

2019

Review of Computer Engineering Research

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Software cost estimation is a critical task in software projects development. It assists project managers and software engineers to plan and manage their resources. However, developing an accurate cost estimation model for a software project is a challenging process. The aim of such a process is to have a better future sight of the project progress and its phases. Another main objective is to have clear project details and specifications to assist stakeholders in managing the project in terms of human resources, assets, software, data and even in the feasibility study. Accurate estimation results with definitely helps the project manager to do better estimation for the project cost, the time required for various project phases and resources or assets. This paper builds a software cost estimation model using machine learning approach. Different machine learning algorithms are applied to two public datasets to predict the software cost in the early stages. Results show that machine learning methods can be used to predict software cost with a high accuracy rate. Contribution/Originality: This study contributes to the existing literature by enhancing the results of thirteen Machine Learning algorithms on two datasets. The evaluation criteria used in this work are R², MAE, RMAE, RAE, and RRSE. The aim of the proposed model is to predict the effort using dataset attributes and compare them with the actual effort in order to measure the error using different criteria.

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M5P model tree based fast fuzzy maximum power point tracker

Cited by 62 publications

References 43 publications

Static and Dynamic Photovoltaic Cell/Module Parameters Identification

Static and Dynamic Photovoltaic Cell/Module Parameters Identification

Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

A Review of Machine Learning Models for Software Cost Estimation

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