预测模型用于英国电网的短期负载预测
1Department of Electrical Engineering, University of Hafr Al Batin, Hafr Al Batin, Kingdom of Saudi Arabia.
PloS one
|April 4, 2024
概括
准确的短期负载预测对于管理电动汽车 (EV) 增加的电力需求至关重要. 机器学习模型,特别是支持向量回归 (SVR) 和人工神经网络 (ANN),在预测电网负载方面表现出高精度.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 能源系统 能源系统
背景情况:
- 全球部署电动汽车 (EV) 正在加速能源转型.
- 越来越多的电动汽车采用引发了人们对由于需求增加而对电网造成潜在压力的担忧.
- 准确的短期负载预测对于高效的电网规划,运营和控制至关重要.
研究的目的:
- 开发和评估强大的机器学习模型,用于英国电力系统的短期负载预测.
- 为了比较支持向量回归 (SVR),人工神经网络 (ANN) 和高斯过程回归 (GPR) 的性能,用于负载预测.
- 评估预测模型对半小时和小时电力需求的准确性.
主要方法:
- 英国电力系统的利用净进口数据涵盖了2010-2020年.
- 应用机器学习技术,包括支持向量回归 (SVR),人工神经网络 (ANN) 和高斯过程回归 (GPR).
- 使用诸如根平均平方误差 (RMSE),平均绝对预测误差 (MAPE),平均绝对偏差 (MAD) 和确定相关性 (R2) 等指标评估模型性能.
主要成果:
- 支持向量回归 (SVR) 显示了半小时负载预测的最高准确性,达到99.85%的R值.
- 人工神经网络 (ANN) 为每小时负载预测提供了最佳性能,R值为99.71%.
- 高斯过程回归 (GPR) 也显示出对两个预测期的强大预测能力.
结论:
- 机器学习方法,包括SVR,ANN和GPR,对于短期负载预测非常可靠和精确.
- 对于半小时预测,SVR模型特别有效,而ANN模型在每小时预测方面表现出色.
- 准确的负载预测对于整合可再生能源和管理电动汽车不断变化的需求至关重要.
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