Evolutive ANFIS training for energy load profile forecast for an IEMS in an automated factory

Cárdenas, Juan José; García, A.; Romeral, J.L.; Kampouropoulos, Konstantinos

doi:10.1109/etfa.2011.6059079

Cited by 19 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The prior research used ANFIS for solving classification problem, while the later one chose ANFIS to predict the values of Longitude and Altitude. Cardenas et al [23] trained the parameters of ANFIS using GA alone for energy load forecast.…”

Section: Training Methods Of Anfismentioning

confidence: 99%

A Review of Training Methods of ANFIS for Applications in Business and Economics

Salleh¹,

Hussain²

2016

IJUNESST

View full text Add to dashboard Cite

Fuzzy Neural Networks (FNNs) techniques have been effectively used in applications that range from medical to mechanical engineering, to business and economics. Despite of attracting researchers in recent years and outperforming other fuzzy systems, Adaptive Neuro-Fuzzy Inference System (ANFIS) still needs effective parameter training and rulebase optimization methods to perform efficiently when the number of inputs increase. Moreover, the standard gradient based learning via two pass learning algorithm is prone slow and prone to get stuck in local minima. Therefore many researchers have trained ANFIS parameters using metaheuristic algorithms however very few have considered optimizing the ANFIS rule-base. Mostly Particle Swarm Optimization (PSO) and its variants have been applied for training approaches used. Other than that, Genetic Algorithm (GA), Firefly Algorithm (FA), Ant Bee Colony (ABC) optimization methods have been employed for effective training of ANFIS networks when solving various problems in the field of business and finance.

show abstract

Section: Training Methods Of Anfismentioning

confidence: 99%

A Review of Training Methods of ANFIS for Applications in Business and Economics

Salleh¹,

Hussain²

2016

IJUNESST

View full text Add to dashboard Cite

show abstract

“…Table 2 summarises some studies which have used metaheuristic methods for training the ANFIS network. [38] PSO PSO Turki, Bouzaida [39] PSO PSO Rini, Shamsuddin [40] PSO PSO Karaboga, Kaya [41] ABC ABC Soto, Melin [42] GA LSE Cardenas, Garcia [43] GA GA…”

Section: Learning Algorithm Of Anfismentioning

confidence: 99%

A Control-Oriented ANFIS Model of Evaporator in a 1-kWe Organic Rankine Cycle Prototype

et al. 2021

View full text Add to dashboard Cite

This paper presents a control-oriented neuro-fuzzy model of brazed-plate evaporators for use in organic Rankine cycle (ORC) engines for waste heat recovery from exhaust-gas streams of diesel engines, amongst other applications. Careful modelling of the evaporator is both crucial to assess the dynamic performance of the ORC system and challenging due to the high nonlinearity of its governing equations. The proposed adaptive neuro-fuzzy inference system (ANFIS) model consists of two separate neuro-fuzzy sub-models for predicting the evaporator output temperature and evaporating pressure. Experimental data are collected from a 1-kWe ORC prototype to train, and verify the accuracy of the ANFIS model, which benefits from the feed-forward output calculation and backpropagation capability of the neural network, while keeping the interpretability of fuzzy systems. The effect of training the models using gradient-descent least-square estimate (GDLSE) and particle swarm optimisation (PSO) techniques is investigated, and the performance of both techniques are compared in terms of RMSEs and correlation coefficients. The simulation results indicate strong learning ability and high generalisation performance for both. Training the ANFIS models using the PSO algorithm improved the obtained test data RMSE values by 29% for the evaporator outlet temperature and by 18% for the evaporator outlet pressure. The accuracy and speed of the model illustrate its potential for real-time control purposes.

show abstract

“…Furthermore, the ANFIS can also get trapped into the local minimum due to the applicability of chain rule [83]. To solve these problems, different CI methods such as GA [84], PSO [85], adaptive PSO [86] and ABC [87] are utilized to update the ANFIS parameters instead of the conventional gradient method in modern MG control architectures.…”

Section: Anfis-based Mg Dynamic Response Enhancementmentioning

confidence: 99%

Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids

et al. 2020

View full text Add to dashboard Cite

The penetration of distributed generators (DGs) in the existing power system has brought some real challenges regarding the power quality and dynamic response of the power systems. To overcome the above-mentioned issues, the researchers around the world have tried and tested different control methods among which the computational intelligence (CI) based methods have been found as most effective in mitigating the power quality and transient response problems intuitively. The significance of the mentioned optimization approaches in contemporary ac Microgrid (MG) controls can be observed from the increasing number of published articles and book chapters in the recent past. However, literature related to this important subject is scattered with no comprehensive review that provides detailed insight information on this substantial development. As such, this research work provides a detailed overview of four of the most extensively used CI-based optimization techniques, namely, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) as applied in ac MG controls from 42 research articles along with their basic working mechanism, merits, and limitations. Due to space and scope constraints, this study excludes the applications of swarm intelligence-based optimization methods in the studied field of research. Each of the mentioned CI algorithms is explored for three major MG control applications i.e., reactive power compensation and power quality, MPPT and MG’s voltage, frequency, and power regulation. In addition, this work provides a classification of the mentioned CI-based optimization studies based on various categories such as key study objective, optimization method applied, DGs utilized, studied MG operating mode, and considered operating conditions in order to ease the searchability and selectivity of the articles for the readers. Hence, it is envisaged that this comprehensive review will provide a valuable one-stop source of knowledge to the researchers working in the field of CI-based ac MG control architectures.

show abstract

Evolutive ANFIS training for energy load profile forecast for an IEMS in an automated factory

Cited by 19 publications

References 15 publications

A Review of Training Methods of ANFIS for Applications in Business and Economics

A Review of Training Methods of ANFIS for Applications in Business and Economics

A Control-Oriented ANFIS Model of Evaporator in a 1-kWe Organic Rankine Cycle Prototype

Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids

Contact Info

Product

Resources

About