An overview of performance evaluation metrics for short-term statistical wind power forecasting

Sopeña, Juan Manuel González; Pakrashi, Vikram; Ghosh, Bidisha

doi:10.1016/j.rser.2020.110515

Cited by 85 publications

(23 citation statements)

References 83 publications

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“…RNN has a great learning advantage for the non-linear characteristics of sequence data. Classification of some intelligent predictors, summarizes their merits and limitations, two auxiliary methods: Ensemble learning and metaheuristic optimization algorithms Vargas et al [13] The evolution of wind energy analysis over the last 30 years, an innovative literature review approach Wang et al [14] Applications of artificial intelligent algorithms in wind farms Gonzalez et al [15] The recently proposed forecasting model, performance evaluation methods Yang et al [16] Three novel technologies, four classifications of wind data, 37 evaluation criteria, 100 methods, 22 sub-categories of forecasting approaches in three perspectives 3.1.1 | Models with long short-term memory predictor LSTM network is designed to solve the vanishing gradient problem that occurs when RNN learns sequences with longterm dependence [66]. Compared to the simple structure of RNN, LSTM is far more complicated.…”

Section: Rnn-based Modelsmentioning

confidence: 99%

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A comprehensive review on deep learning approaches in wind forecasting applications

Luo

Yang

et al. 2022

CAAI Trans on Intel Tech

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The effective use of wind energy is an essential part of the sustainable development of human society, in particular, at the recent unprecedented pressure in shaping a low carbon energy environment. Accurate wind resource and power forecasting play a key role in improving the wind penetration. However, it has not been well adopted in the real-world applications due to the strong stochastic characteristics of wind energy. In recent years, the application boost of deep learning methods provides new effective tools in wind forecasting. This paper provides a comprehensive overview of the forecasting models based on deep learning in the field of wind energy. Featured approaches include timeseries-based recurrent neural networks, restricted Boltzmann machines, convolutional neural networks as well as auto-encoder-based approaches. In addition, future development directions of deep-learning-based wind energy forecasting have also been discussed. K E Y W O R D S deep learning, deep neural networks, learning (artificial intelligence)This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Rnn-based Modelsmentioning

confidence: 99%

“…Gonzalez et al. [15] summarized the commonly used performance indicators for deterministic and probabilistic short‐term wind power forecasting and explained the performance of these indicators on different data sets, time resolutions and certain specific model attributes. Yang et al.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review on deep learning approaches in wind forecasting applications

Luo

Yang

et al. 2022

CAAI Trans on Intel Tech

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“…The performance of the models is measured using one of the most widespread metrics in the WPF literature [18], the mean absolute error (MAE):…”

Section: Performance Evaluationmentioning

confidence: 99%

Decomposition-Based Hybrid Models for Very Short-Term Wind Power Forecasting

Sopeña

Pakrashi

Ghosh

2021

The 7th International Conference on Time Series and Forecasting

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Wind power forecasting is a tool used in the energy industry for a wide range of applications, such as energy trading and the operation of the grid. A set of models known as decomposition-based hybrid models have stood out in recent times due to promising results in terms of performance. As many publications on this matter are found in the literature, a comparison of these models is difficult, because they are tested under different conditions in terms of data, prediction horizon, and time resolution. In this paper, we provide a comparison unifying these parameters using the main decomposition algorithms and a set of artificial neural network-based models for very short-term wind power forecasting (up to 30 min ahead). For this purpose, a case study using data from an Irish wind farm is performed to analyze the models in terms of accuracy and robustness for a variety of wind power generation scenarios.

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“…The combined use of the two previous metrics is considered sufficient for the evaluation of the performance of the models and they have been widely used [38][39][40][41]. Alternatively, for the evaluation of future models, combinations of other metrics could be used [42]. For example, a combination of the Normalized Mean Absolute Error (NMAE) and the Index of Agreement (IoA) could be used.…”

Section: Metrics Used To Compare the Different Modelsmentioning

confidence: 99%

Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind Power of Wind Farms

Velázquez-Medina¹,

Portero-Ajenjo²

2021

Theory of Complexity - Definitions, Models, and Applications

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Due to the low dispatchability of wind power, the massive integration of this energy source in electrical systems requires short-term and very short-term wind farm power output forecasting models to be as efficient and stable as possible. A study is conducted in the present paper of potential improvements to the performance of artificial neural network (ANN) models in terms of efficiency and stability. Generally, current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station, in addition to selecting a fixed number of time periods prior to the forecasting. In this respect, new ANN models are proposed in this paper, which are developed by: varying the number of prior 1-h periods (periods prior to the prediction hour) chosen for the input layer parameters; and/or incorporating in the input layers data from a second weather station in addition to the wind farm reference station. It has been found that the model performance is always improved when data from a second weather station are incorporated. The mean absolute relative error (MARE) of the new models is reduced by up to 7.5%. Furthermore, the longer the forecast horizon, the greater the degree of improvement.

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An overview of performance evaluation metrics for short-term statistical wind power forecasting

Cited by 85 publications

References 83 publications

A comprehensive review on deep learning approaches in wind forecasting applications

A comprehensive review on deep learning approaches in wind forecasting applications

Decomposition-Based Hybrid Models for Very Short-Term Wind Power Forecasting

Optimization of the ANNs Models Performance in the Short-Term Forecasting of the Wind Power of Wind Farms

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