Managing the full Lifecycle of Power Information Systems using HCSOA based LSTM-GRU Model in DevOps practices

The traditional power information scheme operating construction is under the management process, DevOps model power data prediction is performed in this study using a Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Hybrid Sand Cat optimisation (HSCOA), which seeks to minimise computing complexity, is used to choose the weight of the suggested model. In order to increase the precision of power load foretelling and the dependability of power network algorithm in the power system. This paper aims to provide a research basis for the expansion of the power information system’s operation structure through an examination of the application model based on DevOps throughout the system’s entire life cycle, and to encourage the company’s business development. Hybrid model accomplished the accuracy of 96.5 then the precision range as 97.0 besides also the recall rate as 97.0 then the Fl-score as 97.0 and then specificity as 95.7 correspondingly.