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Development of Energy Saving Technologies for Smart Buildings by Using Computer Algebra

机译：使用计算机代数开发智能建筑节能技术

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摘要

Intelligent energy saving and energy efficient technologies are a modern large-scale global trend in the development of energy systems. Accurate estimates of energy savings are important for promoting energy-efficient construction projects and demonstrating their economic potential. The growing digital measurement infrastructure used in commercial buildings increases the availability of high-frequency data that can be employed for anomaly detection, diagnostics of equipment, heating systems, and ventilation, as well as optimization of air conditioning. This implies the application of modern machine learning methods capable of generating more accurate energy consumption forecasts for buildings to improve their energy efficiency. In this paper, based on the gradient boosting model, a method for modeling and forecasting energy consumption of buildings is proposed and computer algorithms for its software implementation in the SymPy computer algebra system are developed. To assess the efficiency of the proposed algorithms, a dataset that characterizes energy consumption of 300 commercial buildings is used. Results of computer simulations show that these algorithms improve the accuracy of energy consumption forecasts in more than 80% of cases as compared to other machine learning algorithms.
机译：智能节能和节能技术是能源系统发展的现代大规模全球趋势。准确的节能估计对于促进节能建设项目并展示其经济潜力至关重要。商业建筑中使用的不断增长的数字测量基础设施增加了高频数据的可用性，可用于异常检测，设备诊断，加热系统和通风，以及空调的优化。这意味着现代机器学习方法的应用能够为建筑物产生更准确的能耗预测，以提高其能效。本文基于梯度升压模型，提出了一种建模和预测建筑能耗的方法，并开发了对Sympy计算机代数系统中的软件实现的计算机算法。为了评估所提出的算法的效率，使用表征300个商业建筑的能量消耗的数据集。计算机仿真结果表明，与其他机器学习算法相比，这些算法提高了超过80％的情况下的能耗预测的准确性。

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  • 来源
    《Programming and Computer Software》 |2020年第5期|324-329|共6页
  • 作者

    Shchetinin E. Yu.;

  • 作者单位

    Financial Univ Govt Russian Federat Leningradskii Pr 49 Moscow 125993 Russia;

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