Wind power generation probabilistic modeling using ensemble learning techniques

Banik, Rita; Das, Priyanath; Ray, Saheli; Biswas, Ankur

doi:10.1016/j.matpr.2020.02.464

Cited by 15 publications

(10 citation statements)

References 17 publications

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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].…”

Section: Related Workmentioning

confidence: 99%

“…Ensemble models proposed in literature vary by the methods used and how output is aggregated. Studies pertaining to both switchable [22] and output-aggregated models [23][24][25][26][27] have been described in recent research papers. As for methods, researchers frequently use variations of regression trees [22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

“…Typically, a LSTM network consists of one input layer, one or two LSTM layers, and a dense output layer. Hidden LSTM layers consist of neurons for which input gate, forget gate and output gate are responsible for selective control of information [24,25]. Detailed information about algorithms incorporated in LSTM networks is presented in these papers.…”

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confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…In addition to the prediction accuracy, the ET method has less training and testing time than the other two methods. Banik et al (2020), used Boosting, Gradient Boosting and Extreme gradient boosting (XGBoost) methods as predictors, outliers were removed in the data set consisting of hourly data from 2014, and the relationship between wind power and meteorological parameters was determined with the Pearson correlation heat map. According to the results, a strong correlation was found between wind power and wind speed, wind direction, temperature and humidity, and other parameters were not included in the model.…”

Section: Literature Surveymentioning

confidence: 99%

Short-Term Wind Power Prediction Approach Based On Bayesian Optimization and Ensemble Learning

Yazici¹,

Boran²

2021

Journal of Intelligent Systems: Theory and Applications

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In wind energy studies, predicting the short-term energy generation amount for wind power plants and determining the production offer to be placed on the market play an important role. In this study an hourly short-term wind power estimation of a wind turbine located in Turkey with an installed power of 3600 kW has been made. Estimation results were evaluated on a seasonal and annual basis. New hybrid models have been developed for short-term wind power prediction, consisting of Bayesian Optimization (BO), Support Vector Regression (SVR), Gaussian Process Regression (GPR), Decision Tree (DT), stacking, and bagging algorithms. In the proposed prediction approach, it is aimed to reduce prediction errors by combining different regression algorithms with the BO method and ensemble algorithms. Unlike other wind prediction studies, BO was used for the first time in the hyperparameter selection of the regression algorithms selected as the basic learner in the study. Bayesian optimized decision tree (BO-DT) with the lowest error values among the base learners, and Bayesian optimized gaussian process regression (BO-GPR) combined with bagging and stacking. The efficiency of ensemble learning algorithms was measured by the statistical measurement methods Normalized Absolute Mean Error (NMAE), Normalized Root of Mean Squares Error (NRMSE), and determination coefficient (R 2 ). According to the results, the bagging method created with the BO-DT took the annual average NRMSE, NMAE, R 2 criteria of 11.045%, 4.880%, 0.899, respectively, and the model with the best performance was selected in terms of both annual and seasonal results.

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A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning

et al. 2022

Water Resour Manage

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Wind power generation probabilistic modeling using ensemble learning techniques

Cited by 15 publications

References 17 publications

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

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

Short-Term Wind Power Prediction Approach Based On Bayesian Optimization and Ensemble Learning

A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning

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