短期学习和深度预测模型用于成本优化和碳排放减少在能源水资源管理
Wei Zhao1, Hao Chen2, Devrim Murat Yazan2
1IEBIS, Department of High-tech Business and Entrepreneurship, Faculty of Behavioural, Management and Social Sciences, University of Twente, the Netherlands; Faculty of Business Administration, Turiba University, Latvia.
Journal of environmental management
|June 9, 2025
概括
本研究引入了一种机器学习方法,用于优化荷兰的能源和水资源管理,将预测准确度提高33%,并扩展情景分析,以提高可持续性.
科学领域:
- 环境科学 环境科学
- 运营研究 运营研究
- 机器学习 机器学习
背景情况:
- 有效的能源和水资源管理对于环境可持续性至关重要.
- 荷兰的多样化的气候条件带来了独特的资源管理挑战.
- 传统的预测模型通常需要大量的数据集,限制了它们的适用性.
研究的目的:
- 为能源-水应用提出一个新的多重目标线性计划.
- 整合少数射击学习和先进的机器学习架构,以提高预测.
- 减少资源管理预测建模中的数据依赖.
主要方法:
- 开发了能源水系统的多重目标线性程序.
- 采用少量学习来有效地扩展有限的数据集.
- 利用深度自回归和其他机器学习模型进行精确的预测.
- 应用机器学习来增强多目标运营研究优化.
主要成果:
- 与传统模型相比,预测准确度提高了33%.
- 成功地将8个已解决的场景扩展到800个类似的场景.
- 在运营效率和可持续性方面取得了显著的改进.
- 展示了机器学习在克服小数据集局限性的有效性.
结论:
- 拟议的综合方法显著提高了资源管理预测的准确性和可靠性.
- 机器学习有效地弥补了小型数据集,确保了模型的可扩展性和有效性.
- 该方法在各种环境场景中为可持续能源和水资源管理提供了一个有希望的解决方案.
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