Abstract:
Complex factors make industrial power load forecasting difficult. Machine-learning methods are used to forecast industrial power load. Thus, a new power load forecasting method takes into account load characteristics in different regions, industries, and production patterns. First, improved K-means clustering classifies historical load data by production pattern. Next, the prediction algorithm combines reinforcement learning, particle swarm optimization, and the least-squares support vector machine. After processing, this article’s improved algorithm forecasts short-term load using load data in different patterns. This article forecasts using real data. The simulation experiment shows that the improved prediction algorithm can distinguish production pattern changes and identify load characteristics of different regions and industries with high prediction accuracy, which has practical application.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
