Estimation of wind speed probability distribution and wind energy potential using adaptive neuro-fuzzy methodology

Asghar, Aamer Bilal; Liu, Xiaodong

doi:10.1016/j.neucom.2018.01.077

Cited by 38 publications

(25 citation statements)

References 39 publications

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“…The MAE is a linear score, which means that all the individual differences are weighted equally in the average. Accordingly, errors of the prediction models were computed using MAE, as shown in Equation (6). In addition, the results of MAE for all countries are presented in Figure 5.…”

Section: Resultsmentioning

confidence: 99%

“…The literature has emphasized the significant role of ANFIS in predicting different application areas such as forecasting the risks in the stock market, Michell [4], predictions of solar radiation, Morshedizadeh, et al [5], and wind speed prediction, Asghar and Liu [6]. But, there are few previous studies which have employed the ANFIS method for predicting CO 2 emissions based on two important indicators including economic growth and energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System

et al. 2018

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Understanding the relationships among CO2 emissions, energy consumption, and economic growth helps nations to develop energy sources and formulate energy policies in order to enhance sustainable development. The present research is aimed at developing a novel efficient model for analyzing the relationships amongst the three aforementioned indicators in G20 countries using an adaptive neuro-fuzzy inference system (ANFIS) model in the period from 1962 to 2016. In this regard, the ANFIS model has been used with prediction models using real data to predict CO2 emissions based on two important input indicators, energy consumption and economic growth. This study made use of the fuzzy rules through ANFIS to generalize the relationships of the input and output indicators in order to make a prediction of CO2 emissions. The experimental findings on a real-world dataset of World Development Indicators (WDI) revealed that the proposed model efficiently predicted the CO2 emissions based on energy consumption and economic growth. The direction of the interrelationship is highly important from the economic and energy policy-making perspectives for this international forum, as G20 countries are primarily focused on the governance of the global economy.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System

et al. 2018

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“…The Weibull distribution model is the most classical model used to fit wind speed frequency distribution [5,[8][9][10][11][12][13][14][15][16]. The model has a strong adaptability to different frequency distribution and can well describe wind speed distribution, especially when estimating wind speed frequency distribution.…”

Section: The Weibull Distribution Modelmentioning

confidence: 99%

“…Vladislovas [15] and his collaborators investigated wind power density distribution at locations with low and high wind speeds using the Weibull model and provided four numerical methods for evaluating Weibull parameters. Asghar and Liu [16] proposed a hybrid intelligent learning based adaptive neuro-fuzzy inference system to accurately estimate Weibull wind speed probability density function.…”

Section: Introductionmentioning

confidence: 99%

Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms

2019

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This study introduces and analyses existing models of wind speed frequency distribution in wind farms, such as the Weibull distribution model, the Rayleigh distribution model, and the lognormal distribution model. Inspired by the shortcomings of these models, we propose a distribution model based on an exponential polynomial, which can describe the actual wind speed frequency distribution. The fitting error of other common distribution models is too large at zero or low wind speeds. The proposed model can solve this problem. The exponential polynomial distribution model can fit multimodal distribution wind speed data as well as unimodal distribution wind speed data. We used the linear-least-squares method to acquire the parameters for the distribution model. Finally, we carried out contrast simulation experiments to validate the effectiveness and advantages of the proposed distribution model.

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“…The initial concepts of evolving intelligent systems (EISs) were conceived around the turn of the 21st century [1]- [3] and now matured [4]. Nowadays, EISs have been widely applied for real-world problems [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

Self-boosting first-order autonomous learning neuro-fuzzy systems

Angelov

2019

Applied Soft Computing

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In this paper, a detailed mathematical analysis of the optimality of the premise and consequent parts of the recently introduced first-order Autonomous Learning Multi-Model (ALMMo) neuro-fuzzy system is conducted. A novel self-boosting algorithm for structure-and parameter-optimization is, then, introduced to the ALMMo, which results in the self-boosting ALMMo (SBALMMo) neuro-fuzzy system. By minimizing the objective functions with the previously collected data, the SBALMMo is able to optimize its system structure and parameters in few iterations. Numerical examples based benchmark datasets and real-world problems demonstrate the effectiveness and validity of the SBALMMo, and show the strong potential of the proposed approach for real applications.

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Estimation of wind speed probability distribution and wind energy potential using adaptive neuro-fuzzy methodology

Cited by 38 publications

References 39 publications

Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System

Energy Consumption, Economic Growth, and CO2 Emissions in G20 Countries: Application of Adaptive Neuro-Fuzzy Inference System

Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms

Self-boosting first-order autonomous learning neuro-fuzzy systems

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