The countermeasures of urban energy risk control oriented to machine learning and data fusion

Abstract

Urban energy is a very important factor in urban development. How to control its energy risk is the key to this article. This article explores the urban energy risk control countermeasures oriented to machine learning and data fusion. This paper understands the connotation and application of machine learning, and then adds data fusion technology to analyze the data, and finally designs the city’s energy risk control countermeasures through risk control. This article explores the relevant information of domestic energy consumption, and then conducts test experiments and analysis on two regions. It turns out that this strategy has the characteristics of reducing urban energy consumption and reducing urban energy risks. Through the use of machine learning and data fusion design of urban energy risk countermeasures, the extremely high risk areas in the two regions have been reduced from 11.82% and 11.80% to 5.39% and 6.12%. And the extremely low risk area increased from 45.94% and 37.65% to 66.29% and 62.12%, this fully shows that the application of machine learning and data fusion in energy risk can well support the distribution and utilization of energy.

Keywords

• Machine learning
• Data fusion
• Energy risk control
• Energy consumption