Intelligent Decision-Making of Adaptive Learning Algorithm in Data Asset Management in Power Industry

In view of the problems of inefficiency and decision lag faced by traditional data asset management methods, an adaptive learning algorithm is proposed to make intelligent decisions on data asset management in the power industry. First, multi-dimensional data such as power production, consumption and equipment status are collected, and data preprocessing technology is used to clean, normalize and extract features to ensure data quality. Then a decision support system based on deep learning is constructed, using LSTM network for load forecasting and combining CNN for equipment status monitoring. Through the online learning mechanism, the system can receive new data in real time and dynamically adjust model parameters to improve prediction accuracy. The minimum mean absolute percentage error (MAPE) of power load predicted by this method is 5.2%. The adaptive learning algorithm greatly improves the flexibility of decision-making by using intelligent information, providing appropriate help for the digitalization of the power industry.