Renewable Energy Risk Assessment with GMM Methodology and Data Measurement Algorithms

Renewable energy is a clean and environmentally friendly energy source. With the increasing global emphasis on environmental protection and sustainable development, renewable energy has become a research hotspot in the energy field. However, the development and application of renewable energy are not smooth sailing, and there are many risks hidden behind it. With the widespread application of renewable energy, data measurement algorithms play an important role in it. This article aimed to explore the risk analysis of renewable energy based on the Gaussian Mixture Model (GMM) method and data measurement algorithms, in order to provide strategies for the utilization of renewable energy and always adhere to the concept of environmentally friendly development. This article introduced the current development status of renewable energy and the importance of risk analysis. This article elaborated on the basic principles and implementation process of GMM method and data measurement algorithm, and illustrated the application of these two methods in renewable energy risk analysis through examples. By using case validation and statistical methods, GMM algorithm and data measurement methods were used to analyze the risks of renewable energy. According to survey data, the theoretical reserve of hydropower resources had an annual power generation capacity of 6943.8 billion kWh. This paper reveals the key factors associated with risk in the renewable energy industry, and provides the potential impact of these factors on the operation and investment of renewable energy projects.