Short term wind power forecasting using machine learning techniques

Chaudhary, Aditya; Sharma, Akash; Kumar, Ayush; Dikshit, Karan; Kumar, Neeraj

doi:10.1080/09720510.2020.1721632

Cited by 18 publications

(8 citation statements)

References 14 publications

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“…Variants of neural network Probabilistic neural network with RNN [23], DBN [24], CGNN [25], RNN [26], NF-GP [27] NF-SC, LSSVR, M5RT [27] Not found Not found Hybrid Models SVR and Hybrid SVR with EPSO-ANN [28], DNN-MRT [29], PCA and DL [30], Hybrid LSTM [31], LSTM-RNN [32] MLR [28], Four multivariate model [31] Yes [32] Not found Non-neural network ML CART-Bagging [33], GBT [34], ML [35], AI-NWP [36], DT, and RF [37] Persistence approach [34], LASSO, kNN, xGBoost, RF and SVR [35], SVM [37] Not found Yes [34] is chosen, the participant will lose its profit during the (Big-Critical) sized dataset duration by not participating in the market, as the model already reached its optimal state after the critical point (in terms of error performance). A dynamic learning approach can be adopted to participate in the market if the model error goes below the acceptable error limit.…”

Section: Category Proposed Methodology Compared Methodologies Correla...mentioning

confidence: 99%

Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction

Zamee

Won

2020

Energies

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A reasonable dataset, which is an essential factor of renewable energy forecasting model development, sometimes is not directly available. Waiting for a substantial amount of training data creates a delay for a model to participate in the electricity market. Also, inappropriate selection of dataset size may lead to inaccurate modeling. Besides, in a multivariate environment, the impact of different variables on the output is often neglected or not adequately addressed. Therefore, in this work, a novel Mode Adaptive Artificial Neural Network (MAANN) algorithm has been proposed using Spearman’s rank-order correlation, Artificial Neural Network (ANN), and population-based algorithms for the dynamic learning of renewable energy sources power generation forecasting model. The proposed algorithm has been trained and compared with three population-based algorithms: Advanced Particle Swarm Optimization (APSO), Jaya Algorithm, and Fine-Tuning Metaheuristic Algorithm (FTMA). Also, the gradient descent algorithm is considered as a base case for comparing with the population-based algorithms. The proposed algorithm has been applied in predicting the power output of a Solar Photovoltaic (PV) and Wind Turbine Energy System (WTES). Using the proposed methodology with FTMA, the error was reduced by 71.261% and 80.514% compared to the conventional fixed-sized dataset gradient descent-based training approach for Solar PV and WTES, respectively.

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Section: Category Proposed Methodology Compared Methodologies Correla...mentioning

confidence: 99%

Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction

Zamee

Won

2020

Energies

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“…P n i¼1 jy true,i À y pred,i j y true,i (12) where n denotes the number of samples andy true,i and y pred,i denotes the actual and predicted value of wind speed value. Lower value of MAE and RMSE shows that the prediction performance is reasonable.…”

Section: Evaluation Metricsmentioning

confidence: 99%

An Efficient and Interpretable Stacked Model for Wind Speed Estimation Based on Ensemble Learning Algorithms

Jha,

Goel,

Kumar

et al. 2024

Energy Tech

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Wind energy has gained tremendous attention recently due to its abundance potential and to obviate the adverse impacts of fossil fuel consumption. However, the efficient development and operation of wind energy systems require precise and accurate wind speed prediction. To fulfill this aim, machine learning algorithms have shown its prowess in accurately predicting the wind speed and this study proposes an interpretable stacked ensemble with ridge regressor utilizing ML algorithms such as catboost (CB), gradient boost (GB), multilayer perceptron (MLP), and random forest (RF) as a base model. The base models’ estimations are integrated by applying a ridge regressor to enhance the accuracy of wind speed estimation. The dataset consists of 87 600 samples from which 80% samples are used for training. The proposed approach provides interpretability of the prediction results through Shapley additive explanations and feature importance analysis. The result of the proposed method is calculated by comparing the estimation outcomes with each model such as CB, GB, MLP, and RF in terms of error metrics such as root mean squared error (RMSE) and mean absolute error (MAE). The proposed model outperforms the others due to a reduction of error in RMSE by 19.89% and MAE by 24% respectively.

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“…Most of the works regarding machine learning techniques and wind power focus on the operational phase of the wind turbines. Mainly, three research lines can be outlined: the prediction of the electrical output, which can be based only on historical data [16], on wind velocity records [17,18]-which can also be predicted using Machine Learning and Artificial Intelligence techniques as hybrid models [19], fuzzy logic [20], Deep Learning [21] or ensemble methods [22]-or on multiple environmental variables [23,24]; the creation of assistant systems for the design and control of wind turbines [25] and wind farms [26][27][28]; and the development of smart and knowledge-based maintenance system for the wind turbines, mainly focused towards fault classification [29,30], anomaly detection [31] and remaining useful life (RUL) estimation [32][33][34]. For an exhaustive review of the machine-learningbased approaches to wind turbine condition monitoring, see Ref.…”

Section: Machine Learning and Wind Turbine Tower Manufacturingmentioning

confidence: 99%

Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study

et al. 2022

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This study analyzes the lead time of the bending operation in the wind turbine tower manufacturing process. Since the operation involves a significant amount of employee interaction and the parts processed are heavy and voluminous, there is considerable variability in the recorded lead times. Therefore, a machine learning regression analysis has been applied to the bending process. Two machine learning algorithms have been used: a multivariate Linear Regression and the M5P method. The goal of the analysis is to gain a better understanding of the effect of several factors (technical, organizational, and experience-related) on the bending process times, and to attempt to predict these operation times as a way to increase the planning and controlling capacity of the plant. The inclusion of the experience-related variables serves as a basis for analyzing the impact of age and experience on the time-wise efficiency of workers. The proposed approach has been applied to the case of a Spanish wind turbine tower manufacturer, using data from the operation of its plant gathered between 2018 and 2021. The results show that the trained models have a moderate predictive power. Additionally, as shown by the output of the regression analysis, there are variables that would presumably have a significant impact on lead times that have been found to be non-factors, as well as some variables that generate an unexpected degree of variability.

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Short term wind power forecasting using machine learning techniques

Cited by 18 publications

References 14 publications

Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction

Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction

An Efficient and Interpretable Stacked Model for Wind Speed Estimation Based on Ensemble Learning Algorithms

Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study

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