Medium- and Long-Term Wind-Power Forecasts, Considering Regional Similarities

Wang, Xianxun; Liu, Yaru; Hou, Jiachen; Wang, Suoping; Yao, Haiyuan

doi:10.3390/atmos14030430

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…Zheng et al [32] combined VMD with neural networks to realize the multi-step prediction of wind speed data. Wang et al [33] considered the similarity characteristics of data between multiple sites to improve the multi-step wind speed prediction accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Zhou,

Ke,

Zhu

et al. 2023

Sustainability

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Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good maintenance plan to guarantee the system’s reliability and reduce the total cost related to maintenance activities at the same time. Because maintenance planning is a long-term decision problem and the wind force is random, long-term wind force prediction is needed to help managers evaluate the loss caused by maintenances to be executed in the future. However, long-term wind force prediction is naturally complicated, which is much harder than the short-term (e.g., day-ahead) prediction widely investigated in the literature. In order to overcome this difficulty, we design a deep learning framework combining variational mode decomposition, a convolution neural network, long short-term memory network, and full-connected network. Using the public data from the city of Leeds, the prediction accuracy of the above framework is validated by comparing it with other prediction techniques. Then, the predicted wind force is input into the established optimization model determining preventive maintenances during a predefined period. Because the uncertainty of wind force is replaced by the prediction value, the optimization model can be established as a mixed-integer linear programing model, which only contains limited variables and can be solved quickly. Lastly, an abundance of numerical experiments are conducted to validate the effectiveness of the proposed optimization model, based on which some managerial insights are provided to the managers of offshore wind farms about the optimal operations and maintenance strategy. The research outcome will greatly promote the development of the wind power industry in the future.

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Section: Literature Reviewmentioning

confidence: 99%

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Zhou,

Ke,

Zhu

et al. 2023

Sustainability

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“…As pointed out by Wilczak et al [29] and Mesa et al [30], wind power estimation has always been of interest to the energy community; however, the main focus has been on improving short-term wind forecasts instead. Moreover, according to Wang et al [31], most regular-and long-term wind power forecasts are primarily designed for individual sites and suffer from certain shortcomings, such as ignoring regional characteristics. Another concern related to extended-range forecasts is that obtaining a computationally robust solution for large-scale wind farms in practice may require the unification of customized tuning approaches, sophisticated optimization models, and accurate machine learning models, as well as the availability of extensive, restricted-access datasets locally acquired from the power plants [13,32].…”

Section: Introductionmentioning

confidence: 99%

Predicting Energy Generation in Large Wind Farms: A Data-Driven Study with Open Data and Machine Learning

Paula,

Casaca,

Colnago

et al. 2023

Inventions

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Wind energy has become a trend in Brazil, particularly in the northeastern region of the country. Despite its advantages, wind power generation has been hindered by the high volatility of exogenous factors, such as weather, temperature, and air humidity, making long-term forecasting a highly challenging task. Another issue is the need for reliable solutions, especially for large-scale wind farms, as this involves integrating specific optimization tools and restricted-access datasets collected locally at the power plants. Therefore, in this paper, the problem of forecasting the energy generated at the Praia Formosa wind farm, an eco-friendly park located in the state of Ceará, Brazil, which produces around 7% of the state’s electricity, was addressed. To proceed with our data-driven analysis, publicly available data were collected from multiple Brazilian official sources, combining them into a unified database to perform exploratory data analysis and predictive modeling. Specifically, three machine-learning-based approaches were applied: Extreme Gradient Boosting, Random Forest, and Long Short-Term Memory Network, as well as feature-engineering strategies to enhance the precision of the machine intelligence models, including creating artificial features and tuning the hyperparameters. Our findings revealed that all implemented models successfully captured the energy-generation trends, patterns, and seasonality from the complex wind data. However, it was found that the LSTM-based model consistently outperformed the others, achieving a promising global MAPE of 4.55%, highlighting its accuracy in long-term wind energy forecasting. Temperature, relative humidity, and wind speed were identified as the key factors influencing electricity production, with peak generation typically occurring from August to November.

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“…present, all countries in the world are facing the pressure of reducing carbon emissions (Granado et al, 2020), and the types of distributed energy resources (DER) have increased greatly, for example, renewable energy sources (RES), battery energy storage systems (BESS), photovoltaic (PV), wind turbines and other new distributed energy (Ding et al, 2022;Wang et al, 2023); New energy vehicles (EV), intelligent buildings and other new electricity units (Zielińska, 2020). Smart Grid Framework 4.0 (Gopstein et al, 2020) will undoubtedly rely on distributed energy resources to meet low energy consumption expectations and achieve the global target of 42% renewable energy generation (U.S. Energy Information Administration, 2021).…”

mentioning

confidence: 99%

Administration strategy of energy management in smart grid: system view and optimization path

Liu,

Wang,

Wei

2023

Front. Energy Res.

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Power generation and transmission infrastructure is vulnerable to the interaction of various Distributed Generations (DG), which leads to the imbalance of power system operation, frequent voltage drops or spikes, and even power outages. This phenomenon not only wastes energy, but also affects grid security. The main reason is a delayed feedback of circuit failure and load changes, and the optimization of energy management system and path is an effective way to solve the above problems. In this paper, a method of multi-objective optimization based on ANFIS algorithm is proposed which can help to improve the demand response, energy storage and management of smart power grid, reduce the volatility of DGs, reducing electricity costs and improving energy efficiency. Firstly, based on the ANFIS algorithm, the distributed power generation control mode, inverter control, real-time electricity price calculation method, energy transfer and storage scheme are improved, and the optimization path of the energy management system is defined. Secondly, the advantages of ANFIS algorithm in response speed and running stability are verified by comparing with other algorithms. Finally, a distributed energy microgrid is constructed for simulation verification. The results show that :(1) ANFIS optimization algorithm has good adaptability in smart grid, and has advantages in large amount of data processing and information transmission; (2) The verification model based on ANFIS has strong elasticity and efficient response speed. The research results will help solve various problems in the smart grid, including establishing a clear energy management system path, maintaining the stable operation of the power system, providing users with more reasonable power plans and the lowest cost of electricity.

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Medium- and Long-Term Wind-Power Forecasts, Considering Regional Similarities

Cited by 7 publications

References 5 publications

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Predicting Energy Generation in Large Wind Farms: A Data-Driven Study with Open Data and Machine Learning

Administration strategy of energy management in smart grid: system view and optimization path

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