The prediction of the wind speed at different heights by machine learning methods

Türkan, Yusuf Sait; Aydoğmuş, Hacer Yumurtacı; Erdal, Hami̇t

doi:10.11121/ijocta.01.2016.00315

Cited by 32 publications

(19 citation statements)

References 26 publications

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“…More recently, machine-learning techniques have been applied to explore their potential in predicting wind speed aloft. Türkan et al (2016) compared the performance of seven machinelearning algorithms in extrapolating the wind resource from 10 to 30 m above ground level (a.g.l.) at a wind farm in Turkey.…”

Section: Introductionmentioning

confidence: 99%

The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds

Bodini

Optis

2020

Wind Energ. Sci.

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Abstract. The extrapolation of wind speeds measured at a meteorological mast to wind turbine rotor heights is a key component in a bankable wind farm energy assessment and a significant source of uncertainty. Industry-standard methods for extrapolation include the power-law and logarithmic profiles. The emergence of machine-learning applications in wind energy has led to several studies demonstrating substantial improvements in vertical extrapolation accuracy in machine-learning methods over these conventional power-law and logarithmic profile methods. In all cases, these studies assess relative model performance at a measurement site where, critically, the machine-learning algorithm requires knowledge of the rotor-height wind speeds in order to train the model. This prior knowledge provides fundamental advantages to the site-specific machine-learning model over the power-law and log profiles, which, by contrast, are not highly tuned to rotor-height measurements but rather can generalize to any site. Furthermore, there is no practical benefit in applying a machine-learning model at a site where winds at the heights relevant for wind energy production are known; rather, its performance at nearby locations (i.e., across a wind farm site) without rotor-height measurements is of most practical interest. To more fairly and practically compare machine-learning-based extrapolation to standard approaches, we implemented a round-robin extrapolation model comparison, in which a random-forest machine-learning model is trained and evaluated at different sites and then compared against the power-law and logarithmic profiles. We consider 20 months of lidar and sonic anemometer data collected at four sites between 50 and 100 km apart in the central United States. We find that the random forest outperforms the standard extrapolation approaches, especially when incorporating surface measurements as inputs to include the influence of atmospheric stability. When compared at a single site (the traditional comparison approach), the machine-learning improvement in mean absolute error was 28 % and 23 % over the power-law and logarithmic profiles, respectively. Using the round-robin approach proposed here, this improvement drops to 20 % and 14 %, respectively. These latter values better represent practical model performance, and we conclude that round-robin validation should be the standard for machine-learning-based wind speed extrapolation methods.

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Section: Introductionmentioning

confidence: 99%

The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds

Bodini

Optis

2020

Wind Energ. Sci.

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“…The MLP is an optimum feed-forward artificial neural network (ANN), trained with the back-propagation algorithm, that consists of neurons with substantially weighted interconnections where signals always travel in the direction of the output layer. These neurons are mapped as sets of input data onto a set of proper outputs with hidden layers (Turkan et al, 2016). The input signals are sent by the input layer to the hidden layer without executing any operations.…”

Section: Multi-layer Perceptron (Mlp) Modelmentioning

confidence: 99%

“…Then, the hidden and output layers multiply the input signals by a set of weights, and either linearly or non-linearly transform the results into output values. The connection between units in following layers has an associated weight (Turkan et al, 2016), and these weights are optimized to compute reasonable prediction accuracy (Elish, 2014;Lek and Park, 2008). A typical MLP with one hidden layer can be described mathematically as follows (Turkan et al…”

Section: Multi-layer Perceptron (Mlp) Modelmentioning

confidence: 99%

Modelling the unsaturated hydraulic conductivity of a sandy loam soil using Gaussian process regression

Al-Dosary

Al-Sulaiman

Aboukarima

2019

WSA

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Unsaturated soil hydraulic conductivity is a main parameter in agricultural and environmental studies, necessary for predicting and managing water and solute transport in soils. This parameter is difficult to measure in agricultural fields; thus, a simple and practical estimation method would be preferable, and quantitative methods (analytical and numerical) to predict the field parameters should be developed. Field experiments were conducted to collect water quality data to model the unsaturated hydraulic conductivity of a sandy loam soil. A mini disk infiltrometer (MDI) was used to measure soil infiltration rate. Input variables included electrical conductivity and the sodium adsorption ratio of irrigation water. Suction rate (pressure head), soil bulk density, and soil moisture content acted as inputs, with unsaturated soil hydraulic conductivity as output. The performance of Gaussian process regression (GPR) was analysed, with multiple linear regression (LR) and multi-layer perceptron (MLP) models used for comparison. Three performance criteria were compared: correlation coefficient (r), root mean square error (RMSE), and mean absolute error (MAE). The simulations employed the Waikato environment for knowledge analysis (WEKA) open source tool. The results indicate that the GPR with Pearson VII function-based universal kernel (PUK kernel), cache size 250007, Omega 1.0 and Sigma 1.0 performs better than other kernels when evaluating test split data, with a correlation coefficient of 0.9646. The RMSEs for GPR (PUK kernel), MLP, and LR were 1.16 × 10−04, 1.87 × 10−04, and 2.22 × 10−04 cm·s−1, respectively. Predictive data mining algorithms (DMA) enable an estimate of unknown values based on patterns in a database. Therefore, the present methodology can be put to use in predictive tools to manage water and solute transport in soils, as the GPR model provides much greater accuracy than the LR and MLP models in predicting the unsaturated hydraulic conductivity of a sandy loam soil.

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“…KStar is also an instance-based classifier developed for regression with a generalized distance function based on transformations (Türkan et al, 2016). The KStar algorithm uses entropic measure, based on probability of transforming instance into another by randomly selecting between all possible transformations (Painuli et al, 2014).…”

Section: Kstar (K*)mentioning

confidence: 99%

Comparing Various Machine Learning Methods for Prediction of Patient Revisit Intention: A Case Study

Demirdöğen

Erdal

Akbaba

2017

Scitech

Self Cite

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Numerous methods have been suggested for analysis of costumer intention, from surveys to statistical models. The most recent couple of years, various machine learning methods have effectively been utilized to costumer-centric decision-making problems. The trend of patient revisit intention analysis has an improved reliance on computerized decision making models. Computerized decisionmaking may never take the place of the hospital managers, but it can provide decision support via a simple questionnaire. In this paper, it is carried on a comparative evaluation of the performance of ten widely used machine learning methods, (i.e., logistic regression, multilayer perceptron, support vector machines, IBk, KStar, locally weighted learning, decisionstump, C4.5., randomtree and reduced error pruning tree) for the aim of suggesting appropriate machine learning techniques in the context of patient revisit intention prediction problem. Experimental results reveal that the C4.5 tree demonstrates to be the most suitable predictive model since it has the highest overall average accuracy (95.24%) and a very low percentage error on both Type I (3.40%) and Type II (23.53%) errors, closely followed by the locally weighted learning (94.44%, 3.43%, 31.58%) and decisionstump (94.05%, 3,85%, 30.00%), whereas the logistic regression and the IBk algorithms appear to be the worst in terms of average accuracy (87.30% and 88.49%, respectively) and Type II error (70.37% and 68.18%, respectively). Besides the randomtree (6.36%) and the IBk (6.09%) algorithms appear to be the worst in terms of type I error. As a result, this study has demonstrated the promising attempt of incorporating sentiment classification into patient revisit intention.

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The prediction of the wind speed at different heights by machine learning methods

Cited by 32 publications

References 26 publications

The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds

The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds

Modelling the unsaturated hydraulic conductivity of a sandy loam soil using Gaussian process regression

Comparing Various Machine Learning Methods for Prediction of Patient Revisit Intention: A Case Study

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