基于机器学习的能源管理和电力预测在具有多个分布式能源的电网连接微电网中
Arvind R Singh1, R Seshu Kumar2, Mohit Bajaj3,4,5
1Department of Electrical Engineering, School of Physics and Electronic Engineering, Hanjiang Normal University, Shiyan, 442000, Hubei, People's Republic of China.
Scientific reports
|August 19, 2024
概括
先进的机器学习,特别是支持向量回归 (SVR),准确预测微电网中的可再生能源发电. 这改善了能源管理,降低了8.4%的成本,并提高了电网稳定性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 可再生能源系统可再生能源系统
背景情况:
- 将可再生能源整合到微电网中,带来了预测和管理方面的挑战.
- 准确的发电预测对于电网稳定性和效率至关重要.
研究的目的:
- 提高微电网的效率和可靠性,使用先进的机器学习来预测发电.
- 评估支持向量回归 (SVR) 与传统模型的性能.
主要方法:
- 开发并应用支持向量回归 (SVR) 模型用于发电预测.
- 利用历史能源生产数据,天气模式和电网条件.
- 将SVR模型性能与使用错误指标的线性回归模型进行比较.
主要成果:
- 在SVR模型中,错误指标显著降低:MSE (2.002太阳能,3.059风能),MAE (0.547太阳能,0.825风能),RMSE (1.415太阳能,1.749风能).
- 导致运营成本减少8.4%,可再生能源利用量增加12%.
- 提高了10%的供需平衡,并减少了15%的峰值负载需求.
结论:
- 在微电网中,SVR模型为可再生能源预测提供了卓越的准确性.
- 这种方法提高了能源管理,降低了成本,并提高了电网稳定性.
- 机器学习,特别是SVR,显示了彻底改变可再生能源整合和管理的巨大潜力.
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