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Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment

机译：基于监督的机器学习模型，可在不同的建筑环境中进行短期，中期和长期的能源预测

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摘要

The substantial measure of energy usage connected to the building atmosphere supports and sustains power usage modeling diligence. Amongst the numerous strategies to elaborate energy methods, supervised based machine learning approaches are immeasurable alternative to circumvent the inconvenience correlated to various engineering and data mining approaches when measured/observed data are accessible. This research depicts an analysis of electricity requirement forecasting by supervised based machine learning models with the limited data information. The power usage or energy consumption data is collected from power transmission and distribution networked organization independent system operator New England for one-year ahead energy forecasting. Moreover, energy consumption data is categorized into monthly, seasonally and yearly basis to foresee the performance for short-term, medium-term and long-term as well. Four-supervised based machine learning models employed for energy forecasting which are: i) Binary Decision Tree; ii) Compact Regression Gaussian Process; iii) Stepwise Gaussian Processes Regression; iv) Generalized Linear Regression Model. The input variables comprise the limited external environmental data, day-type/hour-type and the net energy consumption of various types of load. The output is the total energy demand of the building power usage. Modeling studies are escorted for expected energy demand in future perceptive based on Independent System Operator New England data. The performance evaluation indices applied in evaluating the model's forecasting error are coefficient of variation and mean absolute percentage error. In autumn season, the best MAPE and CV of the binary decision tree is 0.809% and 1.359% respectively for seasonal forecasting, and is 0.989% and 1.601% respectively for yearly forecasting. It is observed that the accuracy in forecasting is modest in the autumn season. In yearly prediction, the MAPE and CV of compact regression Gaussian process, stepwise Gaussian processes regression and generalized linear regression are 3.245% and 3.650%, 4.039% and 4.860%, 5.118% and 5.927% respectively. The machine learning model's performance compared and validated with the actual energy consumption, existing artificial neural network model and the mean absolute percentage error and coefficient of variation found 2.416% and 3.290% respectively for yearly prediction. It is depicted that including the utilization of limited energy usage and environmental data as one of the model's input variables, the electricity forecasting precision is more accurate, precise and can be improved.
机译：连接到建筑物大气的能源使用量的重要度量标准可以支持并维持电力使用量建模的努力。在详尽阐述能源方法的众多策略中，基于监督的机器学习方法是不可估量的替代方案，可以避免在可访问测量/观测数据时与各种工程和数据挖掘方法相关的不便之处。这项研究描绘了在有限的数据信息的监督下，基于监督的机器学习模型对电力需求预测的分析。电力使用或能耗数据是从与输电和配电网络组织独立的系统运营商新英格兰收集的，用于一年的能源预测。此外，能源消耗数据按月，季节和年度进行分类，以预测短期，中期和长期的绩效。用于能量预测的基于四监督的机器学习模型为：i）二元决策树； ii）紧凑回归高斯过程； iii）逐步的高斯过程回归； iv）广义线性回归模型。输入变量包括有限的外部环境数据，日类型/小时类型以及各种类型负载的净能耗。输出是建筑用电量的总能量需求。根据新英格兰独立系统运营商的数据，为模型预测研究保驾护航，以期在未来的预期中达到预期的能源需求。用于评估模型的预测误差的性能评估指标是变异系数和平均绝对百分比误差。在秋季，二元决策树的最佳MAPE和CV进行季节性预测分别为0.809％和1.359％，而进行年度预测则分别为0.989％和1.601％。可以看出，在秋季，预报的准确性不高。在年度预测中，紧凑回归高斯过程，逐步高斯过程回归和广义线性回归的MAPE和CV分别为3.245％和3.650％，4.039％和4.860％，5.118％和5.927％。机器学习模型的性能与实际能耗，现有的人工神经网络模型以及平均绝对百分比误差和变异系数进行了比较和验证，分别得出了年度预测的2.416％和3.290％。描述了将有限的能源使用和环境数据的利用作为模型的输入变量之一，电力预测精度更加准确，精确并且可以提高。

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  • 来源
    《Energy》 |2018年第1期|17-32|共16页
  • 作者

    Tanveer Ahmad; Huanxin Chen; Ronggeng Huang; Guo Yabin; Jiangyu Wang; Jan Shair; Hafiz Muhammad Azeem Akram; Syed Agha Hassnain Mohsan; Muhammad Kazim;

  • 作者单位

    School of Energy and Power Engineering, Huazhong University of Science and Technology;

    School of Energy and Power Engineering, Huazhong University of Science and Technology;

    School of Energy and Power Engineering, Huazhong University of Science and Technology;

    School of Energy and Power Engineering, Huazhong University of Science and Technology;

    School of Energy and Power Engineering, Huazhong University of Science and Technology;

    Department of Electrical Engineering, State Key Laboratory of Power System, Tsinghua University;

    Department of Computer Science and Engineering, Shanghai Jiao Tong University;

    Department of Marine Information Science and Engineering, Zhejiang University;

    School of Astronautics, Harbin Institute of Technology;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Supervised ML models; Energy forecasting; Energy efficiency; Environmental data;

    机译：监督的机器学习模型;能源预测;能源效率;环境数据;

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