Hydropower projects investment estimation artificial intelligence model by similarity measure-based few-shot machine learning

Hydropower is an important and clean renewable energy source. The construction of hydropower projects involves huge investments, and it's important to estimate the investment accurately in the design and planning stage. Machine learning can construct investment estimation models based on a large number of samples. However, in the hydropower industry, given that Engineering Procurement Construction (hereafter EPC) is an emerging investment and construction model, relatively few samples are available for investment estimation of EPC hydropower projects. This study aims to propose a few-shot machine learning method for hydropower project investment estimation. First, a comprehensive similarity index is proposed to select similar projects; after that, a hydropower project investment estimation artificial intelligence model is constructed by integrating an improved support vector machine and swarm intelligence optimization algorithm. In this study, the investment data of 36 EPC hydropower projects are collected. Experiments show that the proposed method can construct a hydropower project investment model using a small number of samples, and the model prediction accuracy is 97.1 %, which is better than the traditional machine learning method. The proposed method helps to provide support for the comparison of investment and construction programs for EPC hydropower projects, thus reducing the investment cost.