With the continuous improvement of the level of informatization and intelligence in the power industry

how to efficiently mine equipment operation data and accurately predict the electricity sales amount has become the key to improving the level of power management and service. This paper proposes an equipment data mining and electricity sales amount forecasting service system based on the cloud computing platform. The system realizes efficient storage and parallel computing of massive data through the cloud platform

and uses big data processing technology and machine learning algorithms to carry out in-depth mining of power equipment operation data

and extract key characteristic information. On this basis

it combines time series analysis and multi-model fusion methods to build a high-precision electricity sales amount prediction model. The system has the functions of data visualization

prediction result feedback and dynamic model optimization

which can provide intelligent decision-making support for power enterprises. The experimental results show that the system is superior to the traditional method in terms of data processing efficiency and prediction accuracy

and has good application prospects and promotion value.