RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution

Shamshirband, Shahaboddin; Mohammadi, Kasra; Chong, Wen Tong; Petković, Dalibor; Porcu, Emilio; Mostafaeipour, Ali; Ch, Sudheer; Sedaghat, Ahmad

doi:10.1007/s00382-015-2682-2

Cited by 50 publications

(10 citation statements)

References 53 publications

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“…For example, Mao and Monahan built the relationships between predictability of surface wind vectors and potential influential factors, such as topographic complexity, mean surface wind vectors and standard deviation and kurtosis of wind components, to analyze surface winds by linear statistical prediction. Shamshirband et al developed a new auto‐regressive model to capture chaotic dynamics of wind speed time series for a short‐term (1‐24 h) forecast. On the other hand, with the capability of self‐learning and self‐adaption, the artificial intelligence methods can capture some nonlinear characteristics of variables and have been used broadly.…”

Section: Introductionmentioning

confidence: 99%

Projected changes in wind speed and its energy potential in China using a high‐resolution regional climate model

et al. 2019

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Following its commitment to Paris Agreement in 2015, China has started to explore potential renewable energy solutions with low carbon emissions to mitigate global warming. Though wind energy is one of the most cost-effective solutions and has been favored for climate policy development around the world, its high sensitivity to climate change raises some critical issues for the long-term effectiveness in providing sustainable energy supply. Particularly, how wind speed and its energy potential in China will change in the context of global warming is still not well understood. In this paper, we simulate the near-surface wind speed over China using the PRECIS regional climate modeling system under different RCP emission scenarios for assessing the possible changes in wind speed and wind energy availability over China throughout the 21 st century. Overall, the PRECIS model can reasonably reproduce the mesoscale climatological near-surface wind speed and directions as documented in reanalysis data across most regions of China, while some local discrepancies are reported in the southwestern regions. In the future, the annual mean wind speed would be decreasing in most regions of China, except for a slightly increase in the southeast. The expected changes in wind speed are characterized with different amplitudes and rates under different RCP emission scenarios. The changes in the spatial distribution of wind speed seem to be sensitive for RCP climate emission scenarios, especially in the late 21 st century. The spatiotemporal changes in wind energy potential exhibit a similar behavior to those in near-surface wind speed, but the magnitudes of these changes are larger. In general, the wind power density is expected to increase by over 5% in winter in the major wind fields in China (ie, Northwest, Northcentral and Northeast), while significant decreases (by about 6% on average) are projected for other seasons (ie, spring, summer and autumn). By contrast, the wind energy potential in the northeast would increase over most months in the year, especially in winter and summer. The results of this research are of great importance for understanding where and to what extent the wind energy can be utilized to contribute renewable energy system development in China in support of its long-term climate change mitigation commitment.

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Section: Introductionmentioning

confidence: 99%

Projected changes in wind speed and its energy potential in China using a high‐resolution regional climate model

et al. 2019

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“…References [64,65] designed an algorithm based on an extreme learning machine (ELM) for computation of shape and scale parameters of WD. The authors in [65] tested the algorithm developed in [64] and compared the results obtained with those obtained from support vector machine (SVM) and genetic programming (GP) for estimation of the same Weibull parameters. The wind density calculated using the wind speeds was computed through Equation 1.…”

Section: Review Of Wind Power Forecasting With the Incorporation Of Nnmentioning

confidence: 99%

Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review

et al. 2019

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Renewable energy sources (RESs) are the replacement of fast depleting, environment polluting, costly, and unsustainable fossil fuels. RESs themselves have various issues such as variable supply towards the load during different periods, and mostly they are available at distant locations from load centers. This paper inspects forecasting techniques, employed to predict the RESs availability during different periods and considers the dispatch mechanisms for the supply, extracted from these resources. Firstly, we analyze the application of stochastic distributions especially the Weibull distribution (WD), for forecasting both wind and PV power potential, with and without incorporating neural networks (NN). Secondly, a review of the optimal economic dispatch (OED) of RES using particle swarm optimization (PSO) is presented. The reviewed techniques will be of great significance for system operators that require to gauge and pre-plan flexibility competence for their power systems to ensure practical and economical operation under high penetration of RESs.

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“…The ELM has an advantage over the conventional data-intelligent models framework due to its role in formulating data-intelligent expert systems for application in real-life situations (e.g., in References [27,28]). The ELM has, over the last five years, been used in solving several problems, such as clustering [34], feature learning, classification, and regression [35] with a significant level of performance and learning capacity [36][37][38][39][40][41][42][43][44].…”

Section: Extreme Learning Machine (Elm) Modelmentioning

confidence: 99%

Estimation at Completion Simulation Using the Potential of Soft Computing Models: Case Study of Construction Engineering Projects

AlHares

Budayan

2019

Symmetry

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“Estimation at completion” (EAC) is a manager's projection of a project's total cost at its completion. It is an important tool for monitoring a project's performance and risk. Executives usually make high-level decisions on a project, but they may have gaps in the technical knowledge which may cause errors in their decisions. In this current study, the authors implemented new coupled intelligence models, namely global harmony search (GHS) and brute force (BF) integrated with extreme learning machine (ELM) for modeling the project construction estimation at completion. GHS and BF were used to abstract the substantial influential attributes toward the EAC dependent variable, whereas the effectiveness of ELM as a novel predictive model for the investigated application was demonstrated. As a benchmark model, a classical artificial neural network (ANN) was developed to validate the new ELM model in terms of the prediction accuracy. The predictive models were applied using historical information related to construction projects gathered from the United Arab Emirates (UAE). The study investigated the application of the proposed coupled model in determining the EAC and calculated the tendency of a change in the forecast model monitor. The main goal of the investigated model was to produce a reliable trend of EAC estimates which can aid project managers in improving the effectiveness of project costs control. The results demonstrated a noticeable implementation of the GHS-ELM and BF-ELM over the classical and hybridized ANN models.

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RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution

Cited by 50 publications

References 53 publications

Projected changes in wind speed and its energy potential in China using a high‐resolution regional climate model

Projected changes in wind speed and its energy potential in China using a high‐resolution regional climate model

Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review

Estimation at Completion Simulation Using the Potential of Soft Computing Models: Case Study of Construction Engineering Projects

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