MATLAB-Based Ship Electrical Load Forecasting System

The MATLAB program for ship power load forecasting has demonstrated superior predictive performance, primarily attributed to its implementation of time-series analysis algorithms and the utilization of high-quality historical electrical consumption data. The core algorithm employs autoregressive integrated moving average (ARIMA) modeling with seasonal adjustments to capture cyclical power usage patterns. To further enhance this program, developers could expand its capabilities by integrating diverse datasets including environmental conditions, voyage profiles, and equipment operational states through additional data preprocessing modules. Implementing machine learning algorithms such as Long Short-Term Memory (LSTM) networks or support vector regression (SVR) could significantly improve forecasting accuracy and computational efficiency. This would enable more precise load predictions through TensorFlow integration with MATLAB's neural network toolbox. Additionally, creating API interfaces for integration with vessel management systems would provide a comprehensive solution for optimizing ship operations, potentially through RESTful web services or OPC UA protocols for real-time data exchange. The program's architecture could be extended to include predictive maintenance features by incorporating failure probability models based on load pattern anomalies.