应用机器和深度学习算法的每小时电力需求季节性预测,对不同因素的影响分析
Heba-Allah Ibrahim El-Azab1,2, R A Swief3, Noha H El-Amary4
1Faculty of Engineering, Ahram Canadian University (ACU), Giza, Egypt. heba_elazab@acu.edu.eg.
Scientific reports
|March 19, 2025
概括
这项研究使用先进的人工智能模型预测每小时的电力需求,考虑季节性和温度影响以提高准确性. 基于自适应神经的模糊推理系统在预测不同场景的电力消耗方面表现出最高的精度.
科学领域:
- 能源系统分析 能源系统分析
- 人工智能在能源中的作用
- 时间序列预测时间序列预测
背景情况:
- 准确的电力需求预测对于电网稳定性和经济效率至关重要.
- 季节性变化和温度显著影响电力消耗模式.
- 现有的预测模型需要改进以提高精度,特别是在复杂的控制领域,如ISO-NE-CA.
研究的目的:
- 开发和评估新英格兰控制区 (ISO-NE-CA) 每小时电力需求的短期季节性预测模型.
- 通过整合机器和深度学习技术来提高预测精度.
- 分析温度对不同季节工作日,周末和节假日电力需求的影响.
主要方法:
- 使用一种混合方法,结合了基于自适应神经的模糊推理系统 (ANFIS),长期短期记忆 (LSTM),门式循环单元 (GRU) 和人工神经网络 (ANN).
- 根据需求模式将数据集分为四个季节,并开发了两个预测场景:工作日和假日.
- 使用平均绝对误差 (MAE),根平均平方误差 (RMSE),规范化根平均平方误差 (NRMSE) 和平均绝对百分比误差 (MAPE) 评估模型性能.
主要成果:
- 综合模型的准确性很高,ANFIS在冬季工作日预测中达到99.90%的准确性.
- 工作日预测的最高MAE为46.6514MWh (春季),而最低则为24.759MWh (夏季).
- 对于假日预测,ANFIS实现了96.50%的准确性 (秋季),LSTM显示了190.880MWh (冬季) 的最高MAE.
结论:
- 使用的机器和深度学习算法提供了有效和准确的短期季节性电力需求预测.
- 温度对电力需求产生重大影响,对不同类型的日期和季节产生不同的边际影响.
- 这项研究强调了ANFIS在ISO-NE-CA中实现更高准确度的电力需求预测的潜力.
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