Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models

García, Jorge L. Tena; Calderón, Erasmo Cadenas; González-A, Gilberto; Heras, Eduardo Rangel; Tshikala, Alain Mbikayi

doi:10.1177/1687814018813464

Cited by 14 publications

(6 citation statements)

References 33 publications

(44 reference statements)

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“…The model achieved good accuracy under different temperature conditions, with regression values exceeding 70%. On the other hand, Wang et al [79] and Garcia et al [80] used NARXNN for WT forecasting. Wang et al [79] used a NARXNN model fed by 10-min SCADA data to predict gear oil temperature, in a bid to detect anomalies in WT.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

On the Use of Indirect Measurements in Virtual Sensors for Renewable Energies: A Review

Benabdesselam,

Dollon,

Zemouri

et al. 2024

Electronics

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In the dynamic landscape of renewable energy, the primary goal continues to be the enhancement of competitiveness through the implementation of cutting-edge technologies. This requires a strategic focus on reducing energy costs and maximizing system performance. Within this framework, the continuous online monitoring of assets is essential for efficient operations, by conducting measurements that describe the condition of various components. However, the execution of these measurements can present technical and economic obstacles. To overcome these challenges, the implementation of indirect measurement techniques emerges as a viable solution. By leveraging measurements obtained in easily accessible areas, these methods enable the estimation of quantities in regions that would otherwise be inaccessible. This approach improves the monitoring process’s efficiency and provides previously unattainable information. Adopting indirect measurement techniques is also cost-effective, allowing the replacement of expensive sensors with existing infrastructure, which cuts down on installation costs and labor. This paper offers a detailed state-of-the-art review by providing an in-depth examination and classification of indirect measurement techniques and virtual sensing methods applied in the field of renewable energies. It also identifies and discusses the existing challenges and limitations within this topic and explores potential future developments.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

On the Use of Indirect Measurements in Virtual Sensors for Renewable Energies: A Review

Benabdesselam,

Dollon,

Zemouri

et al. 2024

Electronics

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“…These models are based on patterns and do not use any predefined mathematical model [5]. Statistical methods include autoregressive moving average (ARMA) [40], autoregressive integrated moving average (ARIMA) [41], fractional-ARIMA [42], seasonal-ARIMA [43], ARMA with exogenous input (ARMAX) [44], grey predictors [45], and exponential smoothing [46].…”

Section: ) Statistical Methodsmentioning

confidence: 99%

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

2019

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The intermittent nature of wind energy raised multiple challenges to the power systems and is the biggest challenge to declare wind energy a reliable source. One solution to overcome this problem is wind energy forecasting. A precise forecast can help to develop appropriate incentives and wellfunctioning electric markets. The paper presents a comprehensive review of existing research and current developments in deterministic wind speed and power forecasting. Firstly, we categorize wind forecasting methods into four broader classifications: input data, timescales , power output, and forecasting method. Secondly, the performance of wind speed and power forecasting models is evaluated based on 634 accuracy tests reported in twenty-eight published articles covering fifty locations of ten countries. From the analysis, the most significant errors were witnessed for the physical models, whereas the hybrid models showed the best performance. Although, the physical models have a large normalized root mean square error values but have small volatility. The hybrid models perform best for every time horizon. However, the errors almost doubled at the medium-term forecast from its initial value. The statistical models showed better performance than artificial intelligence models only in the very short term forecast. Overall, we observed the increase in the performance of forecasting models during the last ten years such that the normalized mean absolute error and normalized root mean square error values reduced to about half the initial values.

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“…The ARIMA model, 1714 a combination of the above-mentioned techniques, is used 1715 in [120] for wind speed modeling. [121] uses a SARIMA 1716 and an NN-based model for daily wind power forecasting for 1717 each next day over a year, and mentions that a pure SARIMA 1718 model was not sufficient and outperformed by the NN-based 1719 model. Again, NN-based models have been used to include 1720 complex connections between data points.…”

Section: A: Ddm In Standalone Digital Twinsmentioning

confidence: 99%

Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions

Stadtmann,

Rasheed,

Kvamsdal

et al. 2023

IEEE Access

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This article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels on a scale from 0-5; 0-standalone, 1-descriptive, 2-diagnostic, 3predictive, 4-prescriptive, 5-autonomous. It then, from an industrial perspective, identifies the current state of the art and research needs in the wind energy sector. It is concluded that the main challenges hindering the realization of highly capable digital twins fall into one of the four categories; standards-related, data-related, model-related, and industrial acceptance related. The article proposes approaches to the identified challenges from the perspective of research institutes and offers a set of recommendations for various stakeholders to facilitate the acceptance of the technology. The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.

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Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models

Cited by 14 publications

References 33 publications

On the Use of Indirect Measurements in Virtual Sensors for Renewable Energies: A Review

On the Use of Indirect Measurements in Virtual Sensors for Renewable Energies: A Review

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions

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