Deep Learning-Based Investment Risk Assessment Model for Distributed Photovoltaic Projects

Abstract

This paper presents a deep learning-based investment risk assessment for PV distribution, a convolutional neural network (CNN) and monitoring process to improve the risk of the truth. The architecture model includes a number of different deletion and fusion strategies, performance parameters, environmental information, and simultaneous financial evaluation. The assessment framework employs a comprehensive risk index system covering technical, environmental, economic, and policy risks. Through a case study of 15 distributed PV installations ranging from 100kW to 2MW across diverse geographical locations, the model demonstrates superior performance with technical risk prediction accuracy reaching 94.5% and financial risk prediction accuracy achieving 92.3%. The use of a new multi-head maintenance mechanism improves feature fusion efficiency, while the adaptive loss function optimizes model training for various risks. The system achieved a 45.8% reduction in business risk and a 38.5% reduction in financial risk through mitigation plans. The experimental results prove the model's performance across a wide range of operations and its ability to generate risk estimates for investment decisions. The proposed system provides practical solutions for quantitative risk assessment in distributed PV projects, leading to more effective risk management in renewable energy systems.