Functional-type single-input-rule-modules connected neural fuzzy system for wind speed prediction

Li, Chengdong; Wang, Li; Zhang, Guiqing; Wang, Huidong; Shang, Fang

doi:10.1109/jas.2017.7510640

Cited by 33 publications

(11 citation statements)

References 31 publications

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“…In addition, many machine learning algorithms (such as random forest regression [74] and support vector regression (SVR) [75]) also have been used to analyze and predict data properties. Moreover, advanced deep learning algorithms have been developed and compared with conventional algorithms to evaluate performances [76][77][78][79][80][81].…”

Section: Further Studymentioning

confidence: 99%

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

Baek

Choi

2020

Applied Sciences

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This paper proposes a deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines. The proposed method utilizes two DNN models that are designed to predict ore production during the morning and afternoon haulage sessions, respectively. The configuration of the input nodes of the DNN models is based on truck-haulage conditions and corresponding operation times. To verify the efficacy of the proposed method, training data for the DNN models were generated by processing packet data collected over the two-month period December 2018 to January 2019. Subsequently, following training under different hidden-layer conditions, it was observed that the prediction accuracy of morning ore production was highest when the number of hidden layers and number of corresponding nodes were four and 50, respectively. The corresponding values of the determination coefficient and mean absolute percentage error (MAPE) were 0.99% and 4.78%, respectively. Further, the prediction accuracy of afternoon ore production was highest when the number of hidden layers was four and the corresponding number of nodes was 50. This yielded determination coefficient and MAPE values of 0.99% and 5.26%, respectively. truck-dispatch techniques have been proposed to simulate truck systems using real-time data collected from mining sites. For example, Tan et al. [39] identified discrete haulage-operation events by analyzing historical GPS truck-tracking data and implemented a truck-dispatch simulation algorithm using the Arena ® software. Chaowasakoo et al. [40] developed a real-time truck-dispatch system that plans truck-haulage strategies using truck-shovel activity-time data obtained from GPS.However, providing a comprehensive and effective truck-haulage systems simulation algorithm is difficult. First, open-pit mines are characterized by frequent changes in loading points during operations along with the use of multiple equipment-movement paths, unlike underground mines [1]. Additionally, several environmental factors, such as the weather, rock characteristics, and road conditions, affect haulage operations [12,41]. Thus, it is difficult to design a simulation algorithm for truck-haulage systems in large open-pit mines, where ore loading and haulage operations are simultaneously performed at multiple locations. Moreover, several types of simulation algorithms can be developed for a haulage plan that changes in real time, and it is difficult to alter these algorithms depending on variations in the haulage plan. Therefore, it is necessary to develop a simulation strategy that understands not only the characteristics of truck-haulage systems but also the sequence of discrete haulage operations. Additionally, the simulation technique must accurately predict ore production and equipment utilization using equipment-tracking data previously accumulated without use of prior-knowledge-based algorithms.Deep learning methods have recently attracted increased research interest because they facilitate easy analysis ...

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Section: Further Studymentioning

confidence: 99%

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

Baek

Choi

2020

Applied Sciences

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“…The offered model is used to the Roy Billinton test system for appropriateness studies. In [65], a new neural fuzzy technique to predict the hourly wind speed. Initially, a neural arrangement is planned for the single-inputrule-modules attached fuzzy inference system for combining the advantages of both the fuzzy and neural network is presented.…”

Section: Overview Of Economic Feasibility Of Microgrid Hybrid Energy mentioning

confidence: 99%

State of the Ability in Research on Microgrid Hybrid Energy Systems

Akhtar¹,

Kinmani²,

Jamil³

2019

JESTR

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The power demand with interest in green power has focused researcher to develop the distributed power generation using wind energy, solar energy, etc. With the fast depleting fossil fuel reserves, energy security and environmental concerns there is a huge requirement of alternate sources of energy to fulfill the present energy demand. Distribution generating system can support the weak grids, maintaining grid power, managing balance power and enhancing the power quality. Any research work base depends on the literature survey and the studies carried out by several kinds of research and their support to research field encourages for more scope of research. The important advantages related to microgrids have led to huge efforts to grow their dispersion in the power systems. While microgrid is fast growing but there are still various challenges to efficiently control, design, and operate microgrids when linked to the main grid, and also when in islanded mode, where wide research actions are ongoing to handle these issues. This paper presents a review of issues concerning integration of renewable energy sources to microgrids and offers a description of research in areas related to economic feasibility of microgrids system, including dynamic analysis of microgrid hybrid system in grid-connected mode, application of power electronics, microgrid operation, and control.

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“…Suratgar and Nikravesh [51] proposed a modern technique of fuzzy linguistic modeling and the integral stability analysis as well. In [52] a fuzzy neural network has been used for the wind speed forecasting. In [53] a comparison between ANFIS and autoregressive method for wind speed/power prediction has been done.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System (NCPRT2FS) for Power Prediction

Tavoosi¹,

Suratgar²,

Menhaj³

et al. 2021

Preprint

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Not only does this paper present a novel type-2 fuzzy system for identification and behavior prognostication of an experimental solar cell set and a wind turbine, but also it brings forward an exquisite technique to acquire an optimal number of membership functions and the corresponding rules. It proposes a seven-layered NCPRT2FS. For fuzzification in the first two layers, Gaussian type-2 fuzzy membership functions with uncertainty in the mean, are exploited. The third layer comprises rule definition and the forth one embeds fulfillment of type reduction. The three last remained layers are the ones in which resultant left–right firing points, two end-points and output all get assessed correspondingly. It should not be neglected off the nutshell that recurrent feedback at the fifth layer exerts delayed outputs ameliorating efficiency of the suggested NCPRT2FS. Later in the paper, a modern structural learning, established on type-2 fuzzy clustering, is held forth. An adaptively rated learning back-propagation algorithm is extended to adjust the parameters ensuring the convergence as well. Eventually, solar cell photo-voltaic and wind turbine are deemed as case studies. The experimental data are exploited and the consequent yields emerge so persuasive.

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Functional-type single-input-rule-modules connected neural fuzzy system for wind speed prediction

Cited by 33 publications

References 31 publications

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

State of the Ability in Research on Microgrid Hybrid Energy Systems

Modeling of Renewable Energy Systems by a Self-Evolving Nonlinear Consequent Part Recurrent Type-2 Fuzzy System (NCPRT2FS) for Power Prediction

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