Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

Ding, Jiale; Chen, Guochu; Yuan, Kuo Ching

doi:10.3390/pr8010109

Cited by 34 publications

(23 citation statements)

References 10 publications

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“…The experimental results prove that CEEMDAN-VMD could effectively extract the complex characteristics of carbon price and further break through the limitation of accuracy. (4) Faced with the instability caused by the randomness of output weights and hidden layer thresholds to ELM, it is necessary to apply optimization algorithms to determine the best values of its parameters [40,41]. Therefore, in this paper, the BES algorithm is applied to select the optimal combination of ELM parameters.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Forecasting Carbon Price with Secondary Decomposition Algorithm and Optimized Extreme Learning Machine

Zhou

Wang

2021

Sustainability

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Carbon trading is a significant mechanism created to control carbon emissions, and the increasing enthusiasm for participation in the carbon trading market has forced the emergence of higher-precision carbon price prediction models. Facing the complexity of carbon price time series, this paper proposes a carbon price forecasting hybrid model based on secondary decomposition and an improved extreme learning machine (ELM). First, the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is utilized to decompose the carbon price several intrinsic modal functions to initially weaken the non-linearity of the original carbon price data. Secondly, the first intrinsic mode function (IMF1) with the strongest volatility is processed by the variational mode decomposition (VMD). Then, the partial autocorrelation function (PACF) is applied to obtain the model input variables for subsequences. Finally, the ELM improved by the bald eagle search (BES) algorithm is utilized to make predictions. In the empirical analysis, five actual datasets from three carbon markets are used to verify the prediction performance of the proposed model. Based on the six evaluation indicators of the predicted results, the proposed model is the best performer among all models, which suggests that CEEMDAN-VMD-BES-ELM is effective and stable in predicting carbon price.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Forecasting Carbon Price with Secondary Decomposition Algorithm and Optimized Extreme Learning Machine

Zhou

Wang

2021

Sustainability

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“…Recent papers addressing wind power forecasts could be broadly classified into 5 categories: papers focused on how to increase NWP accuracy [4][5][6][7][8], good-practice prediction guidelines [9][10][11], comparisons of accuracy across prediction models [12][13][14][15], hybrid and ensemble methods [16][17][18][19][20][21][22][23][24][25][26][27], and conventional methods improved by, among other things, preprocessing [28][29][30][31][32][33][34][35]. At this point, clear distinction should be made between hybrid, ensemble and improved models.…”

Section: Related Workmentioning

confidence: 99%

“…Hybridization [16][17][18][19][20][21] and parallelization [22][23][24][25][26][27] of prediction models use datarefining and error compensation, respectively, as an approach to maximize prediction accuracy. The most common bases for hybrid models in recent literature are ANNs [17][18][19]21] due to their generalization ability, while the most common hybrid add ons would be single optimization methods [16,18,20,21]. With varying implementation, error reduction can be achieved in different ways.…”

Section: Related Workmentioning

confidence: 99%

Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

et al. 2021

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The ability to forecast electricity generation for a small wind turbine is important both on a larger scale where there are many such turbines (because it creates problems for networks managed by distribution system operators) and for prosumers to allow current energy consumption planning. It is also important for owners of small energy systems in order to optimize the use of various energy sources and facilitate energy storage. The research presented here addresses an original, rarely predicted 48 h forecasting horizon for small wind turbines. This topic has been rather underrepresented in research, especially in comparison with forecasts for large wind farms. Wind speed forecasts with a 48 h horizon are also rarely used as input data. We have analyzed the available data to identify potentially useful explanatory variables for forecasting models. Eight sets with increasing data amounts were created to analyze the influence of the types and amounts of data on forecast quality. Hybrid, ensemble and single methods are used for predictions, including machine learning (ML) solutions like long short-term memory (LSTM), multi-layer perceptron (MLP), support vector regression (SVR) and K-nearest neighbours regression (KNNR). Original hybrid methods, developed for research of specific implementations and ensemble methods based on hybrid methods’ decreased errors of energy generation forecasts for small wind turbines in comparison with single methods. The “artificial neural network (ANN) type MLP as an integrator of ensemble based on hybrid methods” ensemble forecasting method incorporates an original combination of predictors. Predictions by this method have the lowest mean absolute error (MAE). In addition, this paper presents an original ensemble forecasting method, called “averaging ensemble based on hybrid methods without extreme forecasts”. Predictions by this method have the lowest root mean square error (RMSE) error among all tested methods. LSTM, a deep neural network, is the best single method, MLP is the second best one, while SVR, KNNR and, especially, linear regression (LR) perform less well. We prove that lagged values of forecasted time series slightly increase the accuracy of predictions. The same applies to seasonal and daily variability markers. Our studies have also demonstrated that using the full set of available input data and the best proposed hybrid and ensemble methods yield the lowest error. The proposed hybrid and ensemble methods are also applicable to other short-time generation forecasting in renewable energy sources (RES), e.g., in photovoltaic (PV) systems or hydropower.

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“…There is relatively fewer research applying machine learning methods for NBA game outcomes prediction and NBA game final score prediction [16][17][18][19][20][21][22][23][24]. Therefore, five machine learning methods, including classification and regression trees (CART) [42][43][44][45][46][47][48][49], random forest (RF) [50][51][52][53][54][55][56][57][58][59][60], stochastic gradient boosting (SGB) [24,45,52,[61][62][63][64][65][66][67], eXtreme gradient boosting (XGBoost) [24,[68][69][70][71][72][73], and extreme learning machine (ELM) [24,[73][74]…”

Section: Introductionmentioning

confidence: 99%

Improving Sports Outcome Prediction Process Using Integrating Adaptive Weighted Features and Machine Learning Techniques

Lee

Wang

et al. 2021

Processes

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Developing an effective sports performance analysis process is an attractive issue in sports team management. This study proposed an improved sports outcome prediction process by integrating adaptive weighted features and machine learning algorithms for basketball game score prediction. The feature engineering method is used to construct designed features based on game-lag information and adaptive weighting of variables in the proposed prediction process. These designed features are then applied to the five machine learning methods, including classification and regression trees (CART), random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and extreme learning machine (ELM) for constructing effective prediction models. The empirical results from National Basketball Association (NBA) data revealed that the proposed sports outcome prediction process could generate a promising prediction result compared to the competing models without adaptive weighting features. Our results also showed that the machine learning models with four game-lags information and adaptive weighting of power could generate better prediction performance.

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Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine

Cited by 34 publications

References 10 publications

Forecasting Carbon Price with Secondary Decomposition Algorithm and Optimized Extreme Learning Machine

Forecasting Carbon Price with Secondary Decomposition Algorithm and Optimized Extreme Learning Machine

Hybrid and Ensemble Methods of Two Days Ahead Forecasts of Electric Energy Production in a Small Wind Turbine

Improving Sports Outcome Prediction Process Using Integrating Adaptive Weighted Features and Machine Learning Techniques

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