Intelligent Power System Management Based on Machine Learning Technology

This scholarly article delineates a novel approach for forecasting wind power load. The proposed methodology bifurcates the forecasting challenge into two principal sub-problems: the sequence modeling of temporal dynamic attributes and the nonlinear mapping of static characteristics. Through the integration of an attention mechanism, these sub-problems are synergistically amalgamated, culminating in a regression of the load value via a multilayer perceptron (MLP) network. This technique leverages the distinct advantages of various feature types, thereby encapsulating the temporal dependencies within series data while concurrently accentuating the representational power of static information, thereby offering a versatile and efficient solution for wind power load prediction. Furthermore, the present study delves into the deployment of the SNERDI power system intelligent management framework, which harnesses machine learning methodologies for the purposes of real-time data procurement and analysis, thereby enhancing power generation planning, equipment maintenance, and resource allocation. The integration of algorithms such as Support Vector Machines (SVM), Long Short-Term Memory networks (LSTM), and Reinforcement Learning has markedly augmented the system's predictive precision and fault anticipation capabilities. Empirical findings indicate that the LSTM model has achieved a daily load forecasting accuracy of 95%, representing an approximate 15 percentage point improvement over conventional methodologies. Additionally, the SVM model has demonstrated an equipment fault prediction accuracy exceeding 90%. The adoption of these advanced technologies has substantially bolstered the power system's safety, stability, resource allocation efficiency, and responsiveness to faults.