一个机器学习模型组合用于跨多个时间视界的混合功率负载预测.
Nikolaos Giamarelos1, Myron Papadimitrakis1, Marios Stogiannos1
1Department of Electrical and Electronic Engineering, University of West Attica, Thivon 250, 122 41 Aigaleo, Greece.
本研究引入了一种使用多个机器学习模型的新智能电网负载预测方法. 整体方法可以准确地预测前24小时的电荷,从而改善电网管理.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 向可再生能源的转变需要先进的智能电网技术.
- 准确的负载预测对于高效的电力系统规划,运行和管理至关重要.
- 传统的电网模型正在向智能电网框架发展.
研究的目的:
- 开发一个新的混合组合预测方案,用于电荷.
- 从15分钟到24小时的多个时间地平线提供准确的预测.
- 提高智能电网运营的可靠性和效率.
主要方法:
- 利用了一组多样化的机器学习模型:神经网络,线性回归,支持向量回归,随机森林和稀疏回归.
- 实施了在线决策机制,以动态权衡最终预测的个别模型性能.
- 通过使用来自高压/中压变电站的现实电荷数据来评估该方案.
主要成果:
- 实现了高精度,R平方值从0.99 (提前15分钟) 到0.79 (提前24小时).
- 与最先进的机器学习方法和其他组合技术相比,已证明的优越或竞争性性能.
- 验证了拟议的混合组合预测方案在实际网格数据上的有效性.
结论:
- 拟议的混合组合负载预测方案对智能电网非常有效.
- 该方法为各种短期和中期地平线提供了可靠和准确的预测.
- 这种方法有助于在不断变化的电力系统中改进规划和运营效率.
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