Grand Challenges in Wind Energy Research

Wood, David Murakami

doi:10.3389/fenrg.2020.624646

Cited by 14 publications

(11 citation statements)

References 23 publications

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“…One aspect would be to determine maximum wind power penetration level in view of frequency response adequacy [2]. Another aspect would be good quality of generation predictions to allow us to properly plan for and increase wind power capacity to above the threshold [3].…”

Section: Related Workmentioning

confidence: 99%

“…This forecasting model of generated hourly power is a function of forecasted wind speed. Form of function is the 4th order polynomial described by Formula (3). Catalogue data of wind turbine power curve were used to develop the function equation, with data points responding to powers for 3-25 m/s wind speeds, with 1 m/s steps.…”

Section: Setmentioning

confidence: 99%

“…It is a type of recurrent ANN. Internal modules different from the traditional RNNs allow LSTM to avoid problems with long-term dependencies [3,24], gradient explosion and vanishing [8,10]. Typically, a LSTM network consists of one input layer, one or two LSTM layers, and a dense output layer.…”

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confidence: 99%

“…Detailed information about algorithms incorporated in LSTM networks is presented in these papers. Between layers, dropout layers can be used to prevent model overfitting [3,8]. The principle of this mechanism for each node is to retain given node with a probability according to Bernoulli distribution, and dropping from network with complementary probability [40].…”

mentioning

confidence: 99%

“…Settable hyperparameters for the LSTM model would be mainly the number of hidden layers and neurons in them and each layer activation function, but also batch size, number of training epochs and dropout degree. Besides those, various model optimizers like AdaGrad, RMSProp or ADAM can be used [3,10,24]. For optimal LSTM model selection, ReLU/ELU/PReLU/LeakyReLU/sigmoid/tanh activation functions were used for hidden layers, and sigmoid/linear/tanh/ ReLU were used for the output layer.…”

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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: Setmentioning

confidence: 99%

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