| 黄运有* **,詹剑锋* **.基千深度学习的短期负荷预测综述[J].高技术通讯(中文),2021,31(3):240~248 |
| 基千深度学习的短期负荷预测综述 |
| A review of short-term load forecasting based on deep learning |
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| DOI：10.3772/j.issn.1002-0470.2021.03.003 |
| 中文关键词: 负荷预测；机器学习；智能电网；深度学习；大数据 |
| 英文关键词: load forecasting, machine learning, smart grid, deep learning, big data |
| 基金项目: |
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| 摘要点击次数: 2676 |
| 全文下载次数: 1332 |
| 中文摘要: |
| 负荷预测是电力系统中最重要的工作之一，准确的负荷预测可以帮助决策者合理地进行电网资源的调度，对保持电网高效、稳定、安全、经济地运行具有重要的作用。 随 着智能电网的发展，用户的用电数据呈指数增长，这促进了负荷预测研究的快速发展。特 别是近年来负荷预测领域的技术巳经发生了巨大的转变，很多传统的负荷预测方法逐渐被更加精确的基于数据驱动的深度学习方法所取代。 本文综述了近年来深度学习方法在短期负荷预测领域的发展，并对深度学习在短期负荷预测中的最新成果进行了总结与深 入分析，最后对短期负荷预测领域未来的发展进行了展望。 |
| 英文摘要: |
| Load forecasting is one of the most important tasks in the power system. Accurate load forecasting is able to help decision makers to reasonably dispatch grid resources, which plays an important role in maintaining efficient, stable, safe, and economic operation of the power grid. With the development of smart grids, the exponential growth of users'electricity consumption data has promoted the rapid development of load forecasting research. Es-pecially in recent years, the technology in the field of load forecasting has undergone huge changes. Many tradition-al load forecasting methods have gradually been replaced by more accurate data-driven deep learning methods. This paper reviews the development of deep learning methods in the short-term load prediction field in recent years, summarizes and analyzes the latest researches of deep learning in short-term load prediction, and finally looks into the future development of the short-term load forecasting research. |
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