"人工智能与数据挖掘应用于短期负荷预测的研究进展"

The focus of the document "人工智能-数据挖掘-基于数据挖掘的短期负荷预测.pdf" is on utilizing data mining techniques for short-term load forecasting. Short-term load forecasting is influenced by various factors such as weather changes, holidays, type of days, major social activities, and emergencies. The difficulty lies in considering not only the characteristics of the load itself as a time series but also the influence of various external factors. Short-term load forecasting plays a crucial role in electricity grid management, allowing for efficient planning and resource allocation. Traditional methods of load forecasting often rely on historical data and statistical models. However, with the advancement of artificial intelligence and data mining techniques, more accurate and reliable forecasts can be achieved. The document discusses the challenges of short-term load forecasting and the potential solutions offered by data mining. By analyzing historical data and identifying patterns and trends, data mining algorithms can predict future load demands with greater accuracy. This can help grid operators anticipate peak usage periods, optimize energy production, and prevent potential blackouts. The abstract of the document emphasizes the complex nature of short-term load forecasting and the need to consider various external factors that can influence load demand. By leveraging data mining techniques, grid operators can make more informed decisions and improve the efficiency and reliability of the electricity grid. Overall, the document provides valuable insights into the application of data mining in short-term load forecasting and highlights the potential benefits of utilizing artificial intelligence in electricity grid management. It underscores the importance of accurate load forecasting in ensuring a stable and resilient energy system.