An application of a feed-forward neural network model for wind speed predictions

Kolokythas, K.; Argiriou, Athanassios A.

doi:10.1080/14786451.2021.1915315

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…We found that the energy production forecasts are significantly improved when we use as inputs (a) the forecasted wind speed values based on 10-minute wind speed and direction measurements and (b) the power curve of the wind turbine, both at the time of the forecast (not during the previous time step). This is because the wind speed forecasted using the proposed algorithm, is much closer to the measured wind speed values (Kolokythas and Argiriou, 2021). The use of past values of the difference between the actual and the ideal energy production, further enhance the forecast skill of the model.…”

Section: Discussionmentioning

confidence: 76%

“…The ANN used is a feed-forward Multiple Layer Perceptron (MLP) (equation ( 1)): Except of the above model, we developed an additional feed-forward neural network that uses as input wind speed and direction data with a 10-minute time step, in order to forecast the produced electric energy, based on 24-hours ahead wind speed forecasts. This model has a single hidden layer and it is trained using the BFGS algorithm, according to the procedure detailed in Kolokythas and Argiriou (2021).…”

Section: Model Selectionmentioning

confidence: 99%

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Optimizing the input vectors of applied artificial neural network models for wind power production forecasting

Kolokythas

Argiriou

2021

Wind Engineering

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The significant increase of wind energy production worldwide revealed the necessity of its accurate forecasting. However, this is a very complex and, despite the progress made, more accurate forecasting methods are still needed. Accurate forecasts will contribute to a better power plant and grid management by solving problems related to the distribution and storage of the produced electricity, maximizing thus the profits of wind energy investments, contributing ultimately to their further enhancement. Here we present the development and validation of selected artificial neural network (ANN) wind energy forecasting models that produce hourly forecasts for 24 hours forecast horizon. The models are developed and validated using wind speed, direction, and energy produced by a wind power plant located at a semi-mountainous area in Western Greece. The ANN forecasts are compared against those of the persistence method using the Root Means Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) statistics.

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Section: Discussionmentioning

confidence: 76%

Section: Model Selectionmentioning

confidence: 99%

Optimizing the input vectors of applied artificial neural network models for wind power production forecasting

Kolokythas

Argiriou

2021

Wind Engineering

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“…Spectral analysis techniques with their unique advantages have been better studied and integrated into coal and chemical applications, showing their potential for predictive applications and good robustness. Feed-forward neural networks [24] have been used in many fields [25][26][27] as mathematical models with simple principles and high computational accuracy in machine learning [28].…”

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confidence: 99%

Rapid detection of coal ash based on machine learning and X-ray fluorescence

Huang

Chen

et al. 2022

PMI

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Real-time testing of coal ash plays a vital role in the chemical, power generation, metallurgical, and coal separation sectors. The rapid online testing of coal ash using radiation measurement as the mainstream technology has problems such as strict coal sample requirements, poor radiation safety, low accuracy, and complicated equipment replacement. In this study, an intelligent detection technique based on feed-forward neural networks and improved particle swarm optimization (IPSO-FNN) is proposed to predict coal quality ash content in a fast, accurate, safe，and convenient manner. The data set was obtained by testing the elemental content of 198 coal samples with X-ray fluorescence (XRF). The types of input elements for machine learning (Si, Al, Fe, K, Ca, Mg, Ti, Zn, Na, P) were determined by combining the X-ray photoelectron spectroscopy (XPS) data with the change in the physical phase of each element in the coal samples during combustion. The mean squared error and coefficient of determination were chosen as the performance measures for the model. The results show that the IPSO algorithm is useful in adjusting the optimal number of nodes in the hidden layer. The IPSO-FNN model has strong prediction ability and good accuracy in coal ash prediction. The effect of the input element content of the IPSO-FNN model on the ash content was investigated, and it was found that the potassium content was the most significant factor affecting the ash content. This study is essential for real-time online, accurate, and fast prediction of coal ash.

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