A novel seasonal grey prediction model with time-lag and interactive effects for forecasting the photovoltaic power generation

Wang, Junjie; Ye, Li; Ding, Xiaoyu; Dang, Yaoguo

doi:10.1016/j.energy.2024.131939

Energy

2024

DOI: 10.1016/j.energy.2024.131939

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A novel seasonal grey prediction model with time-lag and interactive effects for forecasting the photovoltaic power generation

Junjie Wang,

Li Ye,

Xiaoyu Ding

et al.

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Cited by 3 publications

References 42 publications

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Data-driven Cultural and Creative Industry Development Trend Forecast

Fan

2024

Applied Mathematics and Nonlinear Sciences

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Under the deep integration of emerging technologies and industries, big data + cultural and creative industry is not only a simple industrial superposition but also the cooperation and complementarity of natural sciences, humanities, and social sciences. In this paper, the development of intelligent cultural and creative industries is predicted by the gray model, then the residuals are corrected, and finally, the residuals are corrected again by the Markov chain to improve the prediction accuracy. The modified GM(1,1) model and the improved Markov chain are used to predict and analyze the development trend of the data-driven cultural and creative industry, and the actual total output is not much different from the predicted output, which indicates that the method constructed in this paper can reasonably predict the results. According to the prediction method designed in this paper, the development of intelligent cultural and creative industries from 2024 to 2033 is predicted, and from 2029 to 2033, the maximum total output is predicted to be 28.02×107 yuan, 32.77×107 yuan, 38.33×107 yuan, 44.83×107 yuan, and 52.43×107 yuan, respectively, and China’s intelligent cultural and creative industry will enter a stage of rapid development.

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Data-driven Cultural and Creative Industry Development Trend Forecast

Fan

2024

Applied Mathematics and Nonlinear Sciences

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Regional Load Forecasting Scheme for Security Outsourcing Computation

Chen,

Zhao,

et al. 2024

Electronics

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Smart grids generate an immense volume of load data. When analyzed using intelligent technologies, these data can significantly improve power load management, optimize energy distribution, and support green energy conservation and emissions reduction goals. However, in the process of data utilization, a pertinent issue arises regarding potential privacy leakage concerning both regional and individual user power load data. This paper addresses the scenario of outsourcing computational tasks for regional power load forecasting in smart grids, proposing a regional-level load forecasting solution based on secure outsourcing computation. Initially, the scheme designs a secure outsourcing training protocol to carry out model training tasks while ensuring data security. This protocol guarantees that sensitive information, including but not limited to individual power consumption data, remains comprehensively safeguarded throughout the entirety of the training process, effectively mitigating any potential risks of privacy infringements. Subsequently, a secure outsourcing online prediction protocol is devised, enabling efficient execution of prediction tasks while safeguarding data privacy. This protocol ensures that predictions can be made without compromising the privacy of individual or regional power load data. Ultimately, experimental analysis demonstrates that the proposed scheme meets the requirements of privacy, accuracy, and timeliness for outsourcing computational tasks of load forecasting in smart grids.

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Cooperative Control Strategy of Optical Storage System Based on an Alternating Sequence Filter

Han,

Zhang,

Tian

et al. 2024

Energies

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Due to photovoltaic (PV) power generation depending on the environment, its output power is volatile, and effectively dealing with its power fluctuation has become a key concern. Aiming at this problem, this article presents an optical storage cooperative control technology based on an Alternating Sequence Filter (ASF), which controls the power management of the Energy Storage System (ESS) consisting of a vanadium redox battery, battery, and supercapacitor. Firstly, an ASF is designed to stabilize the PV power generation by alternating sequence and improve system response speed. Secondly, according to the output signal of the filter, the charge and discharge of the three energy storage units are dynamically adjusted, and the power fluctuation is compensated in real-time to improve the system stability and conversion efficiency. Finally, the simulation results of actual illumination show that the control strategy calls the ESS to stabilize the power fluctuation, so that the power of the direct current bus is stabilized at about 15 kw, and the fluctuation is maintained between −4.48% and 4.05%. The strategy significantly reduces power fluctuation and improves the dynamic response ability and energy storage utilization of the system.

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A novel seasonal grey prediction model with time-lag and interactive effects for forecasting the photovoltaic power generation

Cited by 3 publications

References 42 publications

Data-driven Cultural and Creative Industry Development Trend Forecast

Data-driven Cultural and Creative Industry Development Trend Forecast

Regional Load Forecasting Scheme for Security Outsourcing Computation

Cooperative Control Strategy of Optical Storage System Based on an Alternating Sequence Filter

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