A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

Famoso, Fabio; Oliveri, Ludovica Maria; Brusca, Sebastian; Chiacchio, Ferdinando

doi:10.3390/en17071627

Cited by 4 publications

(1 citation statement)

References 60 publications

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“…Wind energy is a crucial clean and renewable energy source with approximately 1021 GW installed capacity throughout the whole world at the end of 2023 [1]. Its growth rate over the past decade has been about 8% annually [2]. Technological advancements have improved efficiency and reduced costs, making wind power an increasingly significant player in energy transition and sustainable development.…”

Section: Introductionmentioning

confidence: 99%

Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model

Zhang,

Deng,

Wang

et al. 2024

Energies

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In order to improve wind power utilization efficiency and reduce wind power prediction errors, a combined prediction model of EMD-KPCA-BilSTM-ATT is proposed, which includes a data processing method combining empirical mode decomposition (EMD) and kernel principal component analysis (KPCA), and a prediction model combining bidirectional long short-term memory (BiLSTM) and an attention mechanism (ATT). Firstly, the influencing factors of wind power are analyzed. The quartile method is used to identify and eliminate the original abnormal data of wind power, and the linear interpolation method is used to replace the abnormal data. Secondly, EMD is used to decompose the preprocessed wind power data into Intrinsic Mode Function (IMF) components and residual components, revealing the changes in data signals at different time scales. Subsequently, KPCA is employed to screen the key components as the input of the BiLSTM-ATT prediction model. Finally, a prediction is made taking an actual wind farm in Anhui Province as an example, and the results show that the EMD-KPCAM-BiLSTM-ATT combined model has higher prediction accuracy compared to the comparative model.

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Section: Introductionmentioning

confidence: 99%

Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model

Zhang,

Deng,

Wang

et al. 2024

Energies

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The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines

Emexidis,

Gkonis

2024

Applied Sciences

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Wind power has emerged as a crucial substitute for conventional fossil fuels. The combination of advanced technologies such as the internet of things (IoT) and machine learning (ML) has given rise to a new generation of energy systems that are intelligent, reliable, and efficient. The wind energy sector utilizes IoT devices to gather vital data, subsequently converting them into practical insights. The aforementioned information aids among others in the enhancement of wind turbine efficiency, precise anticipation of energy production, optimization of maintenance approaches, and detection of potential risks. In this context, the main goal of this work is to combine the IoT with ML in the wind energy sector by processing weather data acquired from sensors to predict wind power generation. To this end, three different regression models are evaluated. The models under comparison include Linear Regression, Random Forest, and Lasso Regression, which were evaluated using metrics such as coefficient of determination (R²), adjusted R², mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were taken into consideration as well. After examining a dataset from IoT devices that included weather data, the models provided substantial insights regarding their capabilities and responses to preprocessing, as well as each model’s reaction in terms of statistical performance deviation indicators. Ultimately, the data analysis and the results from metrics and criteria show that Random Forest regression is more suitable for weather condition datasets than the other two regression models. Both the advantages and shortcomings of the three regression models indicate that their integration with IoT devices will facilitate successful energy prediction.

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Analysis of Wind Farm Productivity Taking Wake Loss into Account: Case Study

Zagubień,

Wolniewicz,

Szwochertowski

2024

Energies

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Due to the growing demand for green energy, there is a shortage of land available for the location of wind farms. Therefore, the distances between turbines are being reduced, and the power of the turbines is being increased. This results in increased wake loss. The article describes a study of the impact of wind speed deficit and loss of wind turbine output due to wake loss on the decrease in energy efficiency of a wind farm. Two proposed wind farms, where the maximum number of turbines are located, were analyzed. The facilities were designed for implementation in Central Europe. The basic costs of construction and operation of the wind farms (WFs) were estimated. Based on the results of wind measurements and the performance characteristics of wind turbines, the productivity of the WFs was determined. The impact of removing individual turbines with the largest wake losses from the wind farm on the economic outcome of the project was studied. Evaluation criteria were proposed to quantify losses, which can serve as a benchmark for evaluating other wind farms. It was found that the higher the turbine’s power rating, the faster the payback resulting from the wake losses of a single turbine.

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A Dependability Neural Network Approach for Short-Term Production Estimation of a Wind Power Plant

Cited by 4 publications

References 60 publications

Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model

Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model

The Integration of Internet of Things and Machine Learning for Energy Prediction of Wind Turbines

Analysis of Wind Farm Productivity Taking Wake Loss into Account: Case Study

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