Ultra-Short-Term Wind-Power Forecasting Based on the Weighted Random Forest Optimized by the Niche Immune Lion Algorithm

Niu, Dongxiao; Pu, Di; Dai, Shuyu

doi:10.3390/en11051098

Cited by 31 publications

(14 citation statements)

References 34 publications

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“…Regarding the forecasting horizon, in contrast to our short-term (24 h ahead) and medium-term forecasting (168 h ahead), Niu et al [9] developed an ultra-short-term wind-power forecast based on wavelet decomposition and weighted random forest enhanced by applying the niche immune lion algorithm. In rapport to Niu et al [9], in our study, the training datasets are larger in size, therefore allowing us to develop a long short-term memory meteorological forecasting solution for fine-tuning the accuracy of the predicted meteorological parameters up to each of the wind turbines, subsequently using the accurate numerical weather parameters as inputs to a function fitting neural network in view of obtaining accurate short-term and medium-term predictions of both the produced and consumed energy, succeeding in dealing with the challenging conditions of complex hilly terrain.…”

Section: Discussionmentioning

confidence: 99%

“…Wang et al mentioned in their paper that they made use of the standard genetic algorithm in order to decrease the input's dimensions and employ the autoregressive-moving-average model for correcting the errors, by targeting the enhanced ELM model. Niu et al [9] put forward a model that employs wavelet decomposition and weighted random forest enhanced by applying the niche immune lion algorithm, for the purpose of achieving an ultra-short-term forecasting horizon of wind power. Niu et al mentioned that their proposed model benefits from the advantages brought by each of the individual employed models.…”

Section: Literature Reviewmentioning

confidence: 99%

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Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain

2018

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Accurate forecasting of the produced and consumed electricity from wind farms is an essential aspect for wind power plant operators. In this context, our research addresses small-scale wind farms situated on hilly terrain, with the main purpose of overcoming the low accuracy limitations arising from the wind deflection, caused by the quite complex hilly terrain. A specific aspect of our devised forecasting method consists of incorporating advantages of recurrent long short-term memory (LSTM) neural networks, benefiting from their long-term dependencies, learning capabilities, and the advantages of feed-forward function fitting neural networks (FITNETs) that have the ability to map between a dataset of numeric inputs and a set of numeric targets. Another specific element of our approach consists of improving forecasting accuracy by means of refining the accuracy of the weather data input parameters within the same weather forecast resolution area. The developed method has power plant operators as main beneficiaries, but it can also be successfully applied in order to assess the energy potential of hilly areas with deflected wind, being useful for potential investors who want to build this type of wind farms. The method can be compiled and incorporated in the development of a wide range of customized applications targeting electricity forecasting for small wind farms situated on hilly terrain with deflected wind. The experimental results, the implementation of the developed method in a real production environment, its validation, and the comparison between our proposed method and other ones from the literature, confirm that the developed forecasting method represents an accurate, useful, and viable tool that addresses a gap in the current state of knowledge regarding the necessity for an accurate forecasting method that is able to predict with a high degree of accuracy both the produced and consumed electricity for small wind power plants situated on quite complex hilly terrain with deflected wind.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain

2018

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“…The RF algorithm proposed by Breiman [39] is an improved version of the decision tree learning approach, which integrates the prediction of multiple uncorrelated decision trees [40]. The RF algorithm is based on bagging that builds a large collection of de-correlated trees, and then averages them [41,42].…”

Section: Random Forestsmentioning

confidence: 99%

Estimation of Coastal Currents Using a Soft Computing Method: A Case Study in Galway Bay, Ireland

Ren

Jin

et al. 2019

JMSE

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In order to obtain forward states of coastal currents, numerical models are a commonly used approach. However, the accurate definition of initial conditions, boundary conditions and other model parameters are challenging. In this paper, a novel application of a soft computing approach, random forests (RF), was adopted to estimate surface currents for three analysis points in Galway Bay, Ireland. Outputs from a numerical model and observations from a high frequency radar system were used as inputs to develop soft computing models. The input variable structure of soft computing models was examined in detail through sensitivity experiments. High correlation of surface currents between predictions from RF models and radar data indicated that the RF algorithm is a most promising means of generating satisfactory surface currents over a long prediction period. Furthermore, training dataset lengths were examined to investigate influences on prediction accuracy. The largest improvement for zonal and meridional surface velocity components over a 59-h forecasting period was 14% and 37% of root mean square error (RMSE) values separately. Results indicate that the combination of RF models with a numerical model can significantly improve forecasting accuracy for surface currents, especially for the meridional surface velocity component.

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“…The rated installed capacity of the wind farm is 49.5 MW. Each dataset was divided into a training set and testing set, in which the data of the first 14 days was used as the training set to train the prediction model, and the data of the last day were used as the testing set to estimate the prediction performance of the model [49]. The data statistical description of the wind power and meteorological data is shown in Table A1.…”

Section: Datasetsmentioning

confidence: 99%

Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

Zhou

Huo

et al. 2019

Sustainability

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The randomness and volatility of wind power poses a serious threat to the stability, continuity, and adjustability of the power system when it is connected to the grid. Accurate short-term wind power prediction methods have important practical value for achieving high-precision prediction of wind farm power generation and safety and economic dispatch. Therefore, this paper proposes a novel combined model to improve the accuracy of short-term wind power prediction, which involves grey correlation degree analysis, ESMD (extreme-point symmetric mode decomposition), sample entropy (SampEn) theory, and a hybrid prediction model based on three prediction algorithms. The meteorological data at different times and altitudes is firstly selected as the influencing factors of wind power. Then, the wind power sub-series obtained by the ESMD method is reconstructed into three wind power characteristic components, namely PHC (high frequency component of wind power), PMC (medium frequency component of wind power), and PLC (low frequency component of wind power). Similarly, the wind speed sub-series obtained by the ESMD method is reconstructed into three wind speed characteristic components, called SHC (high frequency component of wind speed), SMC (medium frequency component of wind speed), and SLC (low frequency component of wind speed). Subsequently, the Bat-BP model, Adaboost-ENN model, and ENN (Elman neural network), which have high forecasting accuracy, are selected to predict PHC, PMC, and PLC, respectively. Finally, the prediction results of three characteristic components are aggregated into the final prediction values of the original wind power series. To evaluate the prediction performance of the proposed combined model, 15-min wind power and meteorological data from the wind farm in China are adopted as case studies. The prediction results show that the combined model shows better performance in short-term wind power prediction compared with other models.

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Ultra-Short-Term Wind-Power Forecasting Based on the Weighted Random Forest Optimized by the Niche Immune Lion Algorithm

Cited by 31 publications

References 34 publications

Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain

Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain

Estimation of Coastal Currents Using a Soft Computing Method: A Case Study in Galway Bay, Ireland

Forecasting Models for Wind Power Using Extreme-Point Symmetric Mode Decomposition and Artificial Neural Networks

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