A Multiraters Agreement Index for Ordinal Classification

Jolayemi, E. T.

doi:10.1002/bimj.4710330417

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Figure 4C shows the 95% confidence intervals (CIs) obtained by the proposed hybrid model (SDCS-LSSVM), which indicate that both the upper and lower CI are close to the observed wind speed time series. Table 5 shows that single models cannot be applied to multiple forecasting problems, and model performance under specific conditions should be analyzed and understood and incremental improvements made based on knowledge gained [30][31][32][33][34]. In this paper, our hybrid LSSVM model based on WPT and SDCS performs much better than the single models in terms of its precision and stability, and is an excellent method for 10-min wind speed forecasting in Shandong Province compared with other traditional forecasting models.…”

Section: The Forecasting Results In Sitementioning

confidence: 90%

A Hybrid Wind Speed Forecasting Method and Wind Energy Resource Analysis Based on a Swarm Intelligence Optimization Algorithm and an Artificial Intelligence Model

Fu¹,

Wang²

2018

Sustainability

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Wind power has the most potential for clean and renewable energy development. Wind power not only effectively solves the problem of energy shortages, but also reduces air pollution. In recent years, wind speed time series analyses have increasingly become a concern of administrators and power grid dispatchers searching for a reasonable way to reduce the operating cost of wind farms. However, analyzing wind speed in detail has become a difficult task, because the traditional models sometimes fail to capture data features due to the randomness and intermittency of wind speed. In order to analyze wind speed series in detail, in this paper, an effective and practical analysis system is studied and developed, which includes a data analysis module, a data preprocessing module, a parameter optimization module, and a wind speed forecasting module. Numerical results show that the wind time series analysis system can not only assess wind energy resources of a wind farm, but also master future changes of wind speed, and can be an effective tool for wind farm management and decision-making.

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Section: The Forecasting Results In Sitementioning

confidence: 90%

A Hybrid Wind Speed Forecasting Method and Wind Energy Resource Analysis Based on a Swarm Intelligence Optimization Algorithm and an Artificial Intelligence Model

Fu¹,

Wang²

2018

Sustainability

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“…The Index Agreement is a dimensionless index that measures the fitness of the model, ranging from 0 (worst fitness) to 1 (best fitness) [45].…”

Section: Experiments Ii: Comparison Of Three Optimization Algorithms Tmentioning

confidence: 99%

A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

Yao

Wang

2018

Sustainability

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Abstract:In the last few years, researchers have paid increasing attention to improving the accuracy of wind speed forecasting because of its vital impact on power dispatching and grid security. However, it is difficult to achieve a good forecasting performance due to the randomness and intermittency characteristics of wind speed time series. Current forecasting models based on neural network theory could adapt to various types of time series data; however, these models ignore the importance of data pre-processing and model parameter optimization, which leads to poor forecasting accuracy. In this paper, a new hybrid model is developed for short-term multi-step wind speed forecasting, which includes four modules: (1) the data pre-processing module; (2) the optimization module; (3) the hybrid nonlinear forecasting module and (4) the evaluation module. In order to estimate the forecasting ability of the proposed hybrid model, 10 min wind speed data were applied in this paper as a case study. The experimental results in six real forecasting cases indicate that the proposed hybrid model can provide not only accurate but also stable performance in terms of multi-step wind speed forecasting can be considered an effective tool in planning and dispatching for smart grids.

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Weighted Concordance Analysis

Tricot

Lepage²

1995

Biometrical J

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Using the variance stabilizing technique, a product multinomial model is introduced to generate a new statistic to test observers' uncertainty in a weighted concordance analysis. Distance matrices which follow some specific rules are obtained by linear combinations of hierarchical distance matrices whose elements are equal to 0 or 1 and unit diagonal. The new statistic is compared with the kappa statistic interpreted by considering the covariance matrix generated by the data. By rewriting the test statistic in a barycentric form, one demonstrates how to modify the barycentric coefficients to derive an adequate measure of the interobserver agreement. The methods are illustrated using two examples.

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A Multiraters Agreement Index for Ordinal Classification

Cited by 5 publications

References 11 publications

A Hybrid Wind Speed Forecasting Method and Wind Energy Resource Analysis Based on a Swarm Intelligence Optimization Algorithm and an Artificial Intelligence Model

A Hybrid Wind Speed Forecasting Method and Wind Energy Resource Analysis Based on a Swarm Intelligence Optimization Algorithm and an Artificial Intelligence Model

A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

Weighted Concordance Analysis

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