Toward cost-effective residential energy reduction and community impacts: A data-based machine learning approach

作     者：Adel Naji Badr Al Tarhuni Jun-Ki Choi Salahaldin Alshatshati Seraj Ajena 

作者机构：Department of Mechanical EngineeringUniversity of DaytonDaytonOH 45469-0238USA Department of Chemical EngineeringUniversity of DaytonDaytonOH 45469-0238USA 

出 版 物：《能源与人工智能(英文)》 (Energy and AI)

年 卷 期：2021年第4卷第2期

页      面：66-79页

核心收录：

学科分类：07[理学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 070101[理学-基础数学] 

主　　题：Building energy efficiency Data mining Machine learning Levelized cost of energy saving Energy reduction strategy Economic input-output analysis 

摘      要：Many *** incentivize residential energy reduction through rebates,often in response to state mandates for energy reduction or from a desire to reduce demand to mitigate the need to grow generating *** assumption built into incentive programs is that the least efficient residences will be more likely take advantage of the ***,however,is not always the *** main goal of this study was to determine the potential for prioritized incentivization,i.e.,prioritizing incentives that deliver the greatest energy savings per invest-ment through an entire *** uses a data mining approach that leverages known building and energy characteristics for predicting energy consumption of houses that collectively can be considered representative of all residences within an entire *** this model,it estimates natural gas consumption and savings,and corresponding implementation costs associated with the adoption of the most impactful energy reduction *** resulting savings and cost estimates allow us to develop a sequential energy reduction strategy whereby the most economic measures within the whole utility district are *** results show that an energy reduction of 36%can be achieved at a levelized cost of less than$14 per mmBTU($14,780 per MJ),demonstrating the strong potential of this approach.A corresponding Economic Input–Output Analysis captures the cascading community economic impacts of this *** results show that for the roughly 45,000 single-family residences in the studied region,an initial energy efficiency investment of$26M could result in a total cascading multiplier economic impact of$41M and additional economic impacts of$2.2M for the lifetime of the considered energy efficiency measures.