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  • Open Access


    A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

    Haibo Li*, Yongbo Yu, Zhenbo Zhao, Xiaokang Tang

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 653-676, 2024, DOI:10.32604/cmc.2023.046424

    Abstract Accurate forecasting of time series is crucial across various domains. Many prediction tasks rely on effectively segmenting, matching, and time series data alignment. For instance, regardless of time series with the same granularity, segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy. However, these events of varying granularity frequently intersect with each other, which may possess unequal durations. Even minor differences can result in significant errors when matching time series with future trends. Besides, directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead… More >

  • Open Access


    The Short-Term Prediction of Wind Power Based on the Convolutional Graph Attention Deep Neural Network

    Fan Xiao1, Xiong Ping1, Yeyang Li2,*, Yusen Xu2, Yiqun Kang1, Dan Liu1, Nianming Zhang1

    Energy Engineering, Vol.121, No.2, pp. 359-376, 2024, DOI:10.32604/ee.2023.040887

    Abstract The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large scale. Therefore, wind power forecasting plays a key role in improving the safety and economic benefits of the power grid. This paper proposes a wind power predicting method based on a convolutional graph attention deep neural network with multi-wind farm data. Based on the graph attention network and attention mechanism, the method extracts spatial-temporal characteristics from the data of multiple wind farms. Then, combined with a deep neural network, a convolutional graph… More >

  • Open Access


    Short-Term Prediction of Photovoltaic Power Generation Based on LMD Permutation Entropy and Singular Spectrum Analysis

    Wenchao Ma*

    Energy Engineering, Vol.120, No.7, pp. 1685-1699, 2023, DOI:10.32604/ee.2023.025404

    Abstract The power output state of photovoltaic power generation is affected by the earth's rotation and solar radiation intensity. On the one hand, its output sequence has daily periodicity; on the other hand, it has discrete randomness. With the development of new energy economy, the proportion of photovoltaic energy increased accordingly. In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation, this paper proposes the short-term prediction of photovoltaic power generation based on the improved multi-scale permutation entropy, local mean decomposition and singular spectrum analysis algorithm.… More >

  • Open Access


    Short-Term Prediction of Photovoltaic Power Based on Fusion Device Feature-Transfer

    Zhongyao Du1,*, Xiaoying Chen1, Hao Wang2, Xuheng Wang1, Yu Deng1, Liying Sun1

    Energy Engineering, Vol.119, No.4, pp. 1419-1438, 2022, DOI:10.32604/ee.2022.020283

    Abstract To attain the goal of carbon peaking and carbon neutralization, the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy. However, this approach is hindered by the lack of training data for predicting new grid-connected PV power stations. To overcome this problem, this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations. The proposed model integrates a structure model, heat-dissipation conditions, and the loss coefficients of PV modules. Clear-Sky… More >

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