使用机器学习方法预测家庭能源管理系统的能源消耗

IF 0.1 Q4 CONSTRUCTION & BUILDING TECHNOLOGY Russian Journal of Building Construction and Architecture Pub Date : 2023-06-27 DOI:10.29039/2308-0191-2023-11-2-6-6
D. Koroteev, T. Koroteeva, Jueru Huang
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引用次数: 0

摘要

降低基本建设项目在其生命周期的各个阶段的能耗是建筑行业和住房和公共综合体的紧迫任务。本文探讨了住宅建筑运行中降低能源成本的途径。该研究的目的是开发一种在使用基于机器学习方法的家庭能源管理系统时预测能源成本的方法。“智能家居”系统中包含的所有设备分为三种类型,每种类型都描述了一种计算能耗的方法。家庭能源管理系统的算法是提前一小时接收能源供应商关于其成本的信息,计算所有设备的能耗,并基于强化机器学习方法预测能耗。通过将所选时间的预测结果与实际成本进行比较,计算平均绝对误差和平均绝对误差的百分比,评价所选方法的有效性和预测的可靠性。研究结果表明,使用机器学习强化方法构建基于预测能源消耗随时间变化的家庭能源管理系统是有希望的。
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Forecasting energy consumption in home energy management systems using machine learning method
Reducing energy consumption by capital construction projects at all stages of their life cycle is an urgent task for the construction industry and the housing and communal complex. The article discusses ways to reduce energy costs in the operation of residential buildings. The aim of the study is to develop a methodology for predicting energy costs when using a home energy management system based on the machine learning method. All devices included in the "smart home" system are divided into three types, for each of them a method for calculating energy consumption is described. The algorithm of the home energy management system is to receive information from the energy supplier about their cost an hour in advance, calculate the energy consumption of all devices and predict energy consumption based on the reinforcement machine learning method. The effectiveness of the chosen method and the reliability of forecasting were evaluated by comparing the results with real costs for the selected time and calculating the average absolute error and the average absolute error in percent. The results of the study indicate the promise of using the method of machine learning with reinforcement to build a home energy management system based on forecasting energy consumption over time.
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来源期刊
Russian Journal of Building Construction and Architecture
Russian Journal of Building Construction and Architecture CONSTRUCTION & BUILDING TECHNOLOGY-
自引率
50.00%
发文量
28
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