Modeling climate change impact on wind power resources using adaptive neuro-fuzzy inference system

Nabipour, Narjes; Mosavi, Amir; Hajnal, Éva; Nádai, László; Shamshirband, Shahaboddin; Chau, Kwok Wing

doi:10.1080/19942060.2020.1722241

Cited by 36 publications

(16 citation statements)

References 35 publications

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“…There are essential differences between the human brain and a computer. The human brain can handle and judge fuzzy phenomena. , Fuzziness generally exists in human thinking and language communication, which is a manifestation of uncertainty. When human beings describe natural phenomena, they have a fuzzy concept in their brain.…”

Section: Methodsmentioning

confidence: 99%

Fuzzy Neural Network for Studying Coupling between Drilling Parameters

et al. 2021

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The rate of penetration (ROP) is an index used to measure drilling efficiency. However, it is restricted by many factors, and there is a coupling relationship among them. In this study, the random forest algorithm is used to sort influencing factors in order of feature importance. In this way, less influential factors can be removed. A fuzzy neural network (FNN) is applied to the field of drilling engineering for the first time, aiming at the coupling problem to predict the ROP. Fuzzification is an important part of training and realizing FNN, but research on this topic is currently lacking. In this study, K-means are used to divide the data with high similarity into a fuzzy set, which is used as the initialization parameter for the second layer of the FNN. The data of Shunbei No. 1 and 5 fault zones in Xinjiang are collected and trained. The results show that the mean value of the coefficient of determination R 2 is 0.9668 under 10 experiments, which is higher than those obtained from a back propagation neural network and multilayer perceptron particle swarm optimization methods. Therefore, the effectiveness and feasibility of the model are verified. The proposed model can improve drilling efficiency and save drilling costs.

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Section: Methodsmentioning

confidence: 99%

Fuzzy Neural Network for Studying Coupling between Drilling Parameters

et al. 2021

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“…This model was used for evaluating optimal performance of controller on respective problem 9 . AN ANFIS model was developed recently for modelling climate change impact on wind power 10 . A hybrid Enhanced Elephant Herding Optimization Algorithm (EHOA) and ANFIS collectively known as (EHO-ANFIS) have been developed for modelling of microgrid and optimal allocation of low cost grid 11 .…”

Section: Introductionmentioning

confidence: 99%

An adaptive-neuro fuzzy inference system based-hybrid technique for performing load disaggregation for residential customers

Abbas

Sajjad

Hussain

et al. 2022

Sci Rep

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Effective and efficient use of energy is key to sustainable industrial and economic growth in modern times. Demand-side management (DSM) is a relatively new concept for ensuring efficient energy use at the consumer level. It involves the active participation of consumers in load management through different incentives. To enable the consumers for efficient energy management, it is important to provide them information about the energy consumption patterns of their appliances. Appliance load monitoring (ALM) is a feedback system used for providing feedback to customers about their power consumption of individual appliances. For accessing appliance power consumption, the determination of the operating status of various appliances through feedback systems is necessary. Two major approaches used for ALM are intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). In this paper, a hybrid adaptive-neuro fuzzy inference system (ANFIS) is used as an application for NILM. ANFIS model being sophisticated was difficult to work with, but ANFIS model helps to achieve better results than other competent approaches. An ANFIS system is developed for extracting appliance features and then a fine tree classifier is used for classifying appliances having more than 1 kW power rating based on the extracted feature. Several case studies have been performed using ANFIS on a publicly available United Kingdom Domestic Appliance Level Electricity (UK-Dale dataset). The simulation results obtained from the ANFIS for NILM are compared with relevant literature to show the performance of the proposed technique. The results prove that the novel application of ANFIS gives better performance for solving the NILM problem as compared to the other existing techniques.

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“…The purpose of this original research is to optimize the 3D layout of turbines at the Valfajr Wind site in Manjil, Iran. In other words, this is an attempt to identify the best placement of turbines and their heights as the power plant reaches its optimal operating level [58]. Initially, by considering the wind and geographic information of the region, the wake effect is analyzed with the Jensen method [59,60].…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Layout Optimization with Different Hub Heights in Manjil Wind Farm Using Particle Swarm Optimization

et al. 2021

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Nowadays, optimizing wind farm configurations is one of the biggest concerns for energy communities. The ongoing investigations have so far helped increasing power generation and reducing corresponding costs. The primary objective of this study is to optimize a wind farm layout in Manjil, Iran. The optimization procedure aims to find the optimal arrangement of this wind farm and the best values for the hubs of its wind turbines. By considering wind regimes and geographic data of the considered area, and using the Jensen’s method, the wind turbine wake effect of the proposed configuration is simulated. The objective function in the optimization problem is set in such a way to find the optimal arrangement of the wind turbines as well as electricity generation costs, based on the Mossetti cost function, by implementing the particle swarm optimization (PSO) algorithm. The results reveal that optimizing the given wind farm leads to a 10.75% increase in power generation capacity and a 9.42% reduction in its corresponding cost.

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Modeling climate change impact on wind power resources using adaptive neuro-fuzzy inference system

Cited by 36 publications

References 35 publications

Fuzzy Neural Network for Studying Coupling between Drilling Parameters

Fuzzy Neural Network for Studying Coupling between Drilling Parameters

An adaptive-neuro fuzzy inference system based-hybrid technique for performing load disaggregation for residential customers

Wind Farm Layout Optimization with Different Hub Heights in Manjil Wind Farm Using Particle Swarm Optimization

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