Risk Assessment of Smart Community Heating Reconstruction Project Based on Artificial Neural Network

With the rapid development of urbanization, energy conservation has become an important measure for building a conservation-oriented harmonious society. The smart reconstruction of the heating system in old communities is now an inevitable choice for urban development. However, there is not yet any unified risk assessment system for these smart heating reconstruction projects. By virtue of the advantages of artificial neural network (ANN) in data processing, this paper tries to assess the risks of smart community heating reconstruction projects. Firstly, a risk assessment system was established for smart community heating reconstruction projects, and the sensitivity of the indices was analyzed. Next, the primary and secondary models for risk assessment were constructed, and the reliability of project investment risk assessment was examined. Finally, artificial fish swarm algorithm (AFSA) was adopted to optimize the initial connection weights and thresholds of backpropagation neural network (BPNN), and the AFSA-optimized network was adopted to build a risk assessment model for smart community heating reconstruction projects. The feasibility and effectiveness of the proposed model were verified through experiments.