Very short-term wind forecasting for Tasmanian power generation

Potter, Cameron; Negnevitsky, M

doi:10.1109/pes.2006.1709044

Cited by 74 publications

(90 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…• the Box-Jenkins methodology (Box and Jenkins, 1976) -for examples of application see Brown et al (1984); Sfetsos (2000); • spectral analysis based methods -see, for example, Tilley and McBean (1973); Trivikrama et al (1976); Ghil et al (2002), and, • ANN methods -see for example Sfetsos (2000); Kretzschamar et al (2004); Potter and Negnevitsky (2006).…”

Section: Statistical Methods and Artificial Neural Network (Ann)mentioning

confidence: 99%

The similar days method for predicting near surface wind vectors

Klausner

Kaplan

Fattal

2009

Meteorological Applications

View full text Add to dashboard Cite

A reliable forecast of the wind vector at a given site is an important problem with a broad scope of application. Many uses are in the field of wind power in which prediction of strong winds is important; other uses are in the field of civil engineering, where wind gusts are important to structural integrity. For predicting air pollution events or the hazard zone in case of toxic gas accidents the low wind scenarios are of the highest importance, especially under stable conditions. These low wind scenarios are the situations which meso-scale numerical models encounter difficulties or are often neglected when dealing with wind power tasks. Additional drawbacks of meso-scale numerical models in this context are their lack of site-specific detail and the fact that they are very demanding numerically.The 'similar days' method presented here is a method for the prediction of winds at a given location which works well also for low and variable wind conditions. The method is very efficient numerically: typically, runtime is less than a minute on a single PC. This method is based on using the last few hours' measurements at the given site and comparing it to an historical database. A set of criteria is defined to determine the time series similarity. Those similar days are used to construct the forecast.The similar days method was applied to a total of 5 years of measurements collected in two different locations. Very good agreement was achieved between prediction and measurements.

show abstract

Section: Statistical Methods and Artificial Neural Network (Ann)mentioning

confidence: 99%

The similar days method for predicting near surface wind vectors

Klausner

Kaplan

Fattal

2009

Meteorological Applications

View full text Add to dashboard Cite

show abstract

“…In this step, each neuron corresponds to a single Sugeno-type fuzzy rule. Neurons in this layer compute the firing strength of the rule from fuzzification layer's outputs (Potter and Negnevitsky 2006). In ANFIS, a product operator is applied to assess the conjunction of the rule antecedents.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Comparison of neuro-fuzzy network and response surface methodology pertaining to the viscosity of polymer solutions

Akbari

Mahmood

Tan

et al. 2017

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

This study has utilized the response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS) approaches for the modeling of polymer solution viscosity. In the absence of reports in the previous study on applying these two approaches, the main objective of this study has been to compare the performance of these methods toward the viscosity modeling of a polymer solution. By utilizing RSM technique, the effects of three independent parameters including shear rate, polymer concentration, and sodium chloride concentration on viscosity of polymer solution were examined. The RSM results showed that all the parameters were not equally important in the polymer solution viscosity. Moreover, analysis of variance (ANOVA) was also carried out and indicated that there was no evidence of lack of fit in the RSM model. As a second approach for polymer solution viscosity modeling, ANFIS was utilized with two rules constructed based on the first-order Sugeno fuzzy approach and trained by back propagation neural networks algorithm. High coefficient of determination (R 2 ) values ( [99%) showed that the prediction ability of both the ANFIS and RSM models was good enough for the response when the interpolation ability of the models was considered. In order to evaluate the extrapolation abilities of the two developed models, two data sets lying beyond the originally considered data were also taken into account. The results showed that their extrapolation predictive ability was poor. The reason could simply be the inherent behavior of the polymeric solution, i.e., the correlational structure seen in the sample used in the training step did not continue outside the sample space.

show abstract

“…Kalman Filter [8], [9] Fuzzy Time Series [17], [19] Grey Predictor [10] Self-exciting Threshold Autoregressive [20]- [22] Takagi-Sugeno [11]- [14] Smooth Transition Autoregressive [20]- [22] Discrete Hilbert Transform [15], [16] Markov-switching Autoregressive [20]- [22] Abductive Networks (GMDH) [18] Adaptive Fuzzy Logic Models [23], [24] Adaptive Linear Models [23], [24] ARIMA time series models [25]- [35] Neural Networks [19], [36]- [41] Adaptive Neural Fuzzy Inference System [31], [42], [43] …”

Section: Very-short-term Wind Power Forecastingmentioning

confidence: 99%