Article: Digital energy data analysis and risk assessment under precision agriculture financing model Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.94 pp.1 - 18 Abstract: Traditional agricultural financing models fail to fully integrate multi-dimensional data for accurate energy management and risk assessment. This paper proposes a precision agricultural financing model based on digital energy data analysis and risk assessment. We develop an innovative decision support platform that integrates digital twin technology to build a photovoltaic agricultural scenario and a microgrid energy dispatching simulation system. Furthermore, a spatiotemporal attention Transformer model combined with a multi-dimensional risk factor federated learning fusion mechanism is proposed to accurately predict and assess financing risks. Experimental results show that energy costs constitute 49.7% of total operating costs, and the grid's stable power supply time reaches 94.7%. However, due to equipment aging, the actual battery life of energy storage equipment is only 86.0% of its theoretical 40.0-hour capacity. This approach significantly enhances financing decision-making accuracy and microgrid dispatching efficiency. Inderscience Publishers - linking academia, business and industry through research

Title: Digital energy data analysis and risk assessment under precision agriculture financing model

Authors: Zheng Liu

Addresses: Department of Business, Huanghe S&T University, Zhengzhou, 450000, China

Abstract: Traditional agricultural financing models fail to fully integrate multi-dimensional data for accurate energy management and risk assessment. This paper proposes a precision agricultural financing model based on digital energy data analysis and risk assessment. We develop an innovative decision support platform that integrates digital twin technology to build a photovoltaic agricultural scenario and a microgrid energy dispatching simulation system. Furthermore, a spatiotemporal attention Transformer model combined with a multi-dimensional risk factor federated learning fusion mechanism is proposed to accurately predict and assess financing risks. Experimental results show that energy costs constitute 49.7% of total operating costs, and the grid's stable power supply time reaches 94.7%. However, due to equipment aging, the actual battery life of energy storage equipment is only 86.0% of its theoretical 40.0-hour capacity. This approach significantly enhances financing decision-making accuracy and microgrid dispatching efficiency.

Keywords: precision agriculture; financing model; digital energy; risk assessment.

DOI: 10.1504/IJICT.2026.156125

International Journal of Information and Communication Technology, 2026 Vol.27 No.94, pp.1 - 18

Received: 19 Sep 2025
Accepted: 19 Oct 2025
Published online: 04 Sep 2026 *

Title: Digital energy data analysis and risk assessment under precision agriculture financing model

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