Extraction of Representative Scenarios for Photovoltaic Power With Shared Weight Graph Clustering

Considering the rapid advancements in AI technologies such as reinforcement learning, ChatGPT, and deep learning, this paper conducts a comprehensive survey of the technological landscape of AI in the energy and agriculture sectors. It delineates the evolutionary path of AI technologies in smart grids and precision agriculture, highlighting significant advancements in energy prediction, optimisation of production and consumption, and intelligent management. Furthermore, the paper identifies key AI technologies crucial for the Agricultural Energy Internet (AEI), offering specialised exploration into AI applications for crop cultivation and fisheries, including disease detection, yield prediction, and resource management. The research provides essential theoretical foundations for AI integration in each of these agricultural domains. In addition, the paper envisions the future integration of ChatGPT in coupled modelling of agriculture and energy systems, enhancing synergistic intelligent control, and AI‐driven carbon tracking technologies within the AEI. This study facilitates a greater grasp of the transformative potential of AI in reshaping the nexus of agriculture and energy.

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Stochastic weather simulation based on gate recurrent unit and generative adversarial networks

Han,

Fu,

Chang

et al. 2024

IET Power Electronics

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The weather has a significant impact on power load and power system planning. Stochastic weather simulation is important in the field of power systems. However, due to factors such as long recording years, observation technology, and so on, the historical weather data often have the problem of missing or insufficient. Meteorological data are characterized by changeable, rapid change, and high dimensions. Therefore, it is a challenging task to accurately grasp the law of weather data. This article presents a random weather simulation model based on gate recurrent unit (GRU) and generative adversarial networks (GAN). GRU selectively learns or forgets what was in the previous moment during training; it can learn the previous and current data of the time series data. When combined with the GAN, it will produce data with the same distribution as the original weather data. The proposed method was evaluated on a real weather dataset, and the results show that the proposed method outperforms the other contrast algorithms.

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Extraction of Representative Scenarios for Photovoltaic Power With Shared Weight Graph Clustering

Cited by 4 publications

References 36 publications

Towards net zero: Comprehensive approach for voluntary carbon trading among microgrids

Towards net zero: Comprehensive approach for voluntary carbon trading among microgrids

Revolutionising agri‐energy: A comprehensive survey on the applications of artificial intelligence in agricultural energy internet

Stochastic weather simulation based on gate recurrent unit and generative adversarial networks

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