解决伊拉克的能源挑战:利用人工智能模型预测电力供应和需求
Morteza Aldarraji1, Belén Vega-Márquez1, Beatriz Pontes1
1Dept. Computer Languages & Systems, University of Seville, Seville, 41012, Spain.
Heliyon
|February 20, 2024
概括
准确的电力预测对伊拉克至关重要. 线性回归最好预测需求,而XGBoost在供应预测方面表现出色,有助于能源安全和经济增长.
科学领域:
- 能源系统分析 能源系统分析
- 计算智能是一种计算智能.
- 经济预测 经济预测
背景情况:
- 由于技术进步和人口增长,全球能源需求正在上升.
- 像伊拉克这样的发展中国家面临着能源需求超过发电能力的挑战.
- 有效的电力供应和需求管理对于国家稳定和经济进步至关重要.
研究的目的:
- 评估伊拉克电力供需的先进预测模型.
- 用伊拉克电力部 (2019-2021) 的新型数据集分析模型性能.
- 为改善伊拉克的能源安全和资源分配提供数据驱动的见解.
主要方法:
- 利用了伊拉克电力部的数据集,涵盖了2019-2021年的时间.
- 采用了各种不同的预测模型:线性回归,XGBoost,随机森林,LSTM,TCN和MLP.
- 在多个预测时间段 (24,48,72,168小时) 中评估模型性能.
主要成果:
- 线性回归始终优于其他电力需求预测模型.
- 在电力供应预测方面,XGBoost表现优异.
- 统计分析证实了模型之间的性能差异,供应预测没有明显的对差异.
结论:
- 准确的能源预测对于伊拉克的能源安全,资源管理和政策制定至关重要.
- 该研究提供了减轻电力短缺和促进经济发展的工具.
- 建议包括采用创新方法,整合外部数据,并为伊拉克开发特定区域的模型.
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