Feature selection and hyper parameters optimization for short-term wind power forecast

Hui, Huang; Jia, Rong; Shi, Xiaoyu; Liang, Jun; Dang, Jian

doi:10.1007/s10489-021-02191-y

Cited by 47 publications

(16 citation statements)

References 39 publications

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“…RF and GRA are two typical feature selection methods based on screening. RF, an ensemble-learning algorithm, is used for feature selection by calculating the out-of-bag data of the sampling process [17]. Te steps to calculate the importance of a feature X are as follows.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Short‐Term Wind and Solar Power Prediction Based on Feature Selection and Improved Long‐ and Short‐Term Time‐Series Networks

Wang

et al. 2023

Mathematical Problems in Engineering

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In terms of the problems of high feature dimension and large data redundancy in the wind and solar power prediction method, an improved prediction model is proposed by combining feature selection methods with the long- and short-term time-series network (LSTNet). The long short-term memory (LSTM) unit in the LSTNet model is replaced with the bidirectional long short-term memory (BiLSTM), which enables recursive response training for the states of hidden layers at the start and end of the sequence. For feature selection, both feature screening and dimension reduction methods are considered, including random forest (RF), grey relational analysis (GRA), and principal component analysis (PCA). Finally, based on wind and solar power data, the effectiveness of the proposed methods is verified, where the RF-LSTNet performs the best. For wind power prediction, the mean absolute percentage error is reduced by 29.7% and root mean square error is reduced by 24.1% compared with the traditional LSTNet model, and for solar power prediction, the MAPE is reduced by 12.9% and RMSE is reduced by 3.8%.

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Section: Feature Selection Methodsmentioning

confidence: 99%

Short‐Term Wind and Solar Power Prediction Based on Feature Selection and Improved Long‐ and Short‐Term Time‐Series Networks

Wang

et al. 2023

Mathematical Problems in Engineering

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“…Data standardization aims to ensure the application of a common measurement scale to improve data quality. The standardization formula is given below [32,33]:…”

Section: Data Standardizationmentioning

confidence: 99%

“…This technique allows determining the correlation of each meteorological measurement with solar radiation [14], correlations that can be different with the season. The prediction accuracy can be statistically evaluated using PCC as a metric; a larger PCC intuitively reflects a higher linear correlation between the predicted and true values [33,45].…”

Section: Feature Selectionmentioning

confidence: 99%

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

et al. 2022

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Solar energy is one of the most important renewable energies, with many advantages over other sources. Many parameters affect the electricity generation from solar plants. This paper aims to study the influence of these parameters on predicting solar radiation and electric energy produced in the Salt-Jordan region (Middle East) using long short-term memory (LSTM) and Adaptive Network-based Fuzzy Inference System (ANFIS) models. The data relating to 24 meteorological parameters for nearly the past five years were downloaded from the MeteoBleu database. The results show that the influence of parameters on solar radiation varies according to the season. The forecasting using ANFIS provides better results when the parameter correlation with solar radiation is high (i.e., Pearson Correlation Coefficient PCC between 0.95 and 1). In comparison, the LSTM neural network shows better results when correlation is low (PCC in the range 0.5–0.8). The obtained RMSE varies from 0.04 to 0.8 depending on the season and used parameters; new meteorological parameters influencing solar radiation are also investigated.

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“…The majority of studies on forecasting energy consumption have used traditional econometric models, time-series techniques, and emerging intelligent algorithms [ 12 – 15 ]. Emerging artificial intelligence (AI) approaches use powerful self-learning capacities to capture the complex nonlinear features of energy markets [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Forecasting oil consumption with attention-based IndRNN optimized by adaptive differential evolution

Wang

et al. 2022

Appl Intell

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Accurate prediction of oil consumption plays a dominant role in oil supply chain management. However, because of the effects of the coronavirus disease 2019 (COVID-19) pandemic, oil consumption has exhibited an uncertain and volatile trend, which leads to a huge challenge to accurate predictions. The rapid development of the Internet provides countless online information (e.g., online news) that can benefit predict oil consumption. This study adopts a novel news-based oil consumption prediction methodology–convolutional neural network (CNN) to fetch online news information automatically, thereby illustrating the contribution of text features for oil consumption prediction. This study also proposes a new approach called attention-based JADE-IndRNN that combines adaptive differential evolution (adaptive differential evolution with optional external archive, JADE) with an attention-based independent recurrent neural network (IndRNN) to forecast monthly oil consumption. Experimental results further indicate that the proposed news-based oil consumption prediction methodology improves on the traditional techniques without online oil news significantly, as the news might contain some explanations of the relevant confinement or reopen policies during the COVID-19 period.

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Feature selection and hyper parameters optimization for short-term wind power forecast

Cited by 47 publications

References 39 publications

Short‐Term Wind and Solar Power Prediction Based on Feature Selection and Improved Long‐ and Short‐Term Time‐Series Networks

Short‐Term Wind and Solar Power Prediction Based on Feature Selection and Improved Long‐ and Short‐Term Time‐Series Networks

Solar Radiation Forecasting by Pearson Correlation Using LSTM Neural Network and ANFIS Method: Application in the West-Central Jordan

Forecasting oil consumption with attention-based IndRNN optimized by adaptive differential evolution

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