A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids
Given the vitality of the renewable-energy grid market, the optimal allocation of clean energy is crucial. An optimal dispatching method for source–load coordination of renewable-energy grid is proposed. An improved K-means clustering algorithm is used to preprocess the source data and historical load data. A support vector machine is used to predict the cluster of renewable-energy grid resources and load data, and typical scenarios are selected from the prediction results. Taking typical scenarios as a representative, the probability distribution of wind power output is accurately obtained. An optimization model of the total operation cost of the renewable-energy grid is established. The experimental results show that the algorithm reduces the error between the predicted value and the actual value. Our method can improve the real-time prediction accuracy of the renewable-energy grid system and increase the economic benefits of the renewable energy grid.
Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids
Given the vitality of the renewable-energy grid market, the optimal allocation of clean energy is crucial. An optimal dispatching method for source–load coordination of renewable-energy grid is proposed. An improved K-means clustering algorithm is used to preprocess the source data and historical load data. A support vector machine is used to predict the cluster of renewable-energy grid resources and load data, and typical scenarios are selected from the prediction results. Taking typical scenarios as a representative, the probability distribution of wind power output is accurately obtained. An optimization model of the total operation cost of the renewable-energy grid is established. The experimental results show that the algorithm reduces the error between the predicted value and the actual value. Our method can improve the real-time prediction accuracy of the renewable-energy grid system and increase the economic benefits of the renewable energy grid.
Machine Learning Techniques for Decarbonizing and Managing Renewable Energy Grids
Muqing Wu (author) / Qingsu He (author) / Yuping Liu (author) / Ziqiang Zhang (author) / Zhongwen Shi (author) / Yifan He (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Decarbonizing: Chat with Toolmakers
| TIBKAT | 2021
Decarbonizing Cities by Improving Public Transport and Managing Land Use and Traffic
| TIBKAT | 2021
Decarbonizing Tunnel Design and Construction
| Springer Verlag | 2022