English
norskBlar i forfatter Artikler, rapporter og annet (teknologi og sikkerhet) "Chen, Hao"
Viser treff 1-12 av 12
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Assessing probabilistic modelling for wind speed from numerical weather prediction model and observation in the Arctic
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-04-07)Mapping Arctic renewable energy resources, particularly wind, is important to ensure the transition into renewable energy in this environmentally vulnerable region. The statistical characterisation of wind is critical for effectively assessing energy potential and planning wind park sites and is, therefore, an important input for wind power policymaking. In this article, different probability density ... -
Cluster-based ensemble learning for wind power modeling from meteorological wind data
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-17)Reliable and efficient power modeling from meteorological wind data is vital for optimal implementation and monitoring of wind energy, and it is important for understanding turbine control, farm operational optimization, and grid load balance. Based on the idea that similar wind conditions lead to similar wind powers; this paper constructs a modeling scheme that orderly integrates three types of ... -
Comparative study of data-driven short-term wind power forecasting approaches for the Norwegian Arctic region
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-04-26)This paper conducts a systemic comparative study on univariate and multivariate wind power forecasting for five wind farms inside the Arctic area. The development of wind power in the Arctic can help reduce greenhouse gas emissions in this environmentally fragile region. In practice, wind power forecasting is essential to maintain the grid balance and optimize electricity generation. This study first ... -
A comprehensive statistical analysis for residuals of wind speed and direction from numerical weather prediction for wind energy
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-07-30)Wind data are vital for the research in renewable energy research. Their quality from numerical weather prediction significantly influences the wind energy models. This paper utilizes a comprehensive statistical analysis for analyzing predictive errors, named residuals of wind speed and direction modeled by numerical weather prediction models. The analysis, taken an Arctic wind site as an example, ... -
Data-augmented sequential deep learning for wind power forecasting
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-15)Accurate wind power forecasting plays a critical role in the operation of wind parks and the dispatch of wind energy into the power grid. With excellent automatic pattern recognition and nonlinear mapping ability for big data, deep learning is increasingly employed in wind power forecasting. However, salient realities are that in-situ measured wind data are relatively expensive and inaccessible and ... -
An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-12-23)Wind power forecasting is crucial for wind power systems, grid load balance, maintenance, and grid operation optimization. The utilization of wind energy in the Arctic regions helps reduce greenhouse gas emissions in this environmentally vulnerable area. In the present study, eight various models, seven of which are representative machine learning algorithms, are used to make 1, 2, and 3 step ... -
Examination of turbulence impacts on ultra-short-term wind power and speed forecasts with machine learning
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-25)Wind turbines’ economic and secure operation can be optimized through accurate ultra-short-term wind power and speed forecasts. Turbulence, considered as a local short-term physical wind phenomenon, affects wind power generation. This paper investigates the use of turbulence intensity for ultra-short-term predictions of wind power and speed with a wind farm in the Arctic, including and excluding ... -
Exploiting more robust and efficacious deep learning techniques for modeling wind power with speed
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-27)Abstract Sound analyses of the nonlinear relationship between wind speed and power generation are crucial for the advancement of wind energy optimization. As an emerging artificial intelligence technology, deep learning has received growing attention from energy researchers for its outstanding ability to provide complex mappings. However, deep neural networks involve complex configurations, ... -
Impact of increasing share of wind energy on Nordic electrical energy market predictions
(Conference object; Konferansebidrag, 2022-10-14) -
Machine learning forecasts of Scandinavian numerical weather prediction wind model residuals with control theory for wind energy
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-08-22)The quality of wind data from the numerical weather prediction significantly influences the accuracy of wind power forecasting systems for wind parks. Therefore, an in-depth investigation of these wind data themselves is essential to improve wind power generation efficiency and maintain grid reliability. This paper proposes a novel framework based on machine learning for concurrently analyzing and ... -
Noise-intensification data augmented machine learning for day-ahead wind power forecast
(Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-10)The day-ahead wind power forecast is essential for the designation of dispatch schedules for the grid and rational arrangement for production planning by power generation companies. This paper specifically investigates the effect of adding noise to the original wind data for forecasting models. Linear regression, artificial neural networks, and adaptive boosting predictive models based on ... -
Probability distributions for wind speed volatility characteristics: A case study of Northern Norway
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11)The Norwegian Arctic is rich in wind resources. The development of wind power in this region can boost green energy and also promote local economies. In wind power engineering, it is a tremendous advantage to base projects on a sound understanding of the intrinsic properties of wind resources in an area. Wind speed volatility, a phenomenon that strongly affects wind power generation, has not received ...
