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建筑热负荷短期预测的混合预测模型比较评审Comparative review of hybrid forecasting models for short-term prediction of building thermal load

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以下内容为 AI 自动翻译，英文原文附后供参考。

摘要

最近的一篇论文对旨在短期预测建筑热负荷的混合预测模型进行了比较评审。研究强调了各种模型的性能和方法，最终确定EMD-LSTM-Markov模型为最准确的日预测供暖和生活热水需求的模型。然而，它指出该模型在处理高功率波动时面临挑战，表明其应用中存在改进空间。该评审为希望通过更好的预测技术提高建筑管理能效的研究人员和从业者提供了宝贵的资源。

A recent paper has conducted a comparative review of hybrid forecasting models aimed at short-term prediction of building thermal loads. The study highlights the performance and methodologies of various models, ultimately identifying the EMD-LSTM-Markov model as the most accurate for day-ahead predictions of heating and domestic hot water demands. However, it notes that this model faces challenges when dealing with high power swells, indicating areas for improvement in its application. The review serves as a valuable resource for researchers and practitioners looking to enhance energy efficiency in building management through better forecasting techniques.

要点

  1. EMD-LSTM-Markov模型是日预测热负荷的最准确模型。

    The EMD-LSTM-Markov model is the most accurate for day-ahead thermal load predictions.

  2. 混合模型在提高建筑能源需求预测准确性方面显示出潜力。

    Hybrid models show promise in improving forecasting accuracy for building energy demands.

  3. 在处理极端功率条件时仍然存在挑战，强调了进一步研究的必要性。

    Challenges remain in handling extreme power conditions, highlighting the need for further research.

为何重要

改善建筑热负荷的预测模型可以带来显著的能源节约和提高建筑管理的效率，促进可持续发展努力。

Improving forecasting models for building thermal loads can lead to significant energy savings and enhanced efficiency in building management, contributing to sustainability efforts.

按谁的角度看

产品经理

产品经理应考虑将先进的预测模型整合到他们的能源管理解决方案中，以为客户提供更准确的预测并优化能源使用。

Product managers should consider integrating advanced forecasting models into their energy management solutions to provide clients with more accurate predictions and optimize energy usage.

开发者

开发人员可以利用这项研究的见解来增强现有的预测算法或创建新的模型，以解决识别出的局限性，特别是在处理高功率波动方面。

Developers can leverage insights from this research to enhance existing forecasting algorithms or create new models that address the limitations identified, particularly in handling high power swells.

学生与求职者

在能源管理和数据科学领域的学生和求职者可以通过理解这些混合模型受益，因为它们代表了一个具有实际应用的可持续发展研究的增长领域。

Students and job-seekers in the fields of energy management and data science can benefit from understanding these hybrid models, as they represent a growing area of research with practical applications in sustainability.

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