Influence of Rebound Effect on Energy Saving in Smart Homes

2018 ◽  
pp. 266-274
Author(s):  
Ko-jung Chen ◽  
Ziyang Li ◽  
Ta-Ping Lu ◽  
Pei-Luen Patrick Rau ◽  
Dinglong Huang
Keyword(s):  
Energy Saving ◽  
Rebound Effect ◽  
Smart Homes

scholarly journals Evaluation of Sector Wise Energy Saving and Energy Rebound Effect in Bangladesh by Three Dimensional Decomposition Method

2010 ◽  
Vol 5 (2) ◽  
pp. 85-91 ◽  
Author(s):  
Shaikh Khosruzzam ◽  
M. Ali Asgar ◽  
Naimul Karim ◽  
M. Arif Asgar ◽  
Shawkat Akbar
Keyword(s):  
Energy Saving ◽  
Decomposition Method ◽  
Rebound Effect ◽  
Three Dimensional ◽  
Dimensional Decomposition

Should we fear the rebound effect in smart homes?

2020 ◽  
Vol 125 ◽  
pp. 109798
Author(s):  
Julien Walzberg ◽  
Thomas Dandres ◽  
Nicolas Merveille ◽  
Mohamed Cheriet ◽  
Réjean Samson
Keyword(s):  
Rebound Effect ◽  
Smart Homes

scholarly journals A futuristic view of energy saving and related energy rebound effect in Bangladesh using complete decomposition model (CDM)

2012 ◽  
Vol 47 (3) ◽  
pp. 313-320
Author(s):  
S Khosruzzaman ◽  
MA Asgar ◽  
MA Asgar ◽  
KMR Rahman ◽  
S Akbar
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Service Sector ◽  
Rebound Effect ◽  
Structural Effect ◽  
Total Energy Consumption ◽  
Intensity Effect ◽  
Complete Decomposition ◽  
Decomposition Model ◽  
Activity Effect

In this paper Complete Decomposition Model is used to compute the future energy saving pattern from the difference of the 'trend' and 'real' values of energy consumption. The 'trend' is defined as a sum of activity effect and the energy use in the base year. The 'real' is defined as a sum of energy consumption in the base year and the change in energy consumption due to the activity effect, structural effect and intensity effect. This analysis is carried out in respect of Bangladesh for the period 2008-2030. The economic sectors that are taken in to account are agriculture, industry and service. The futuristic view shows that Bangladesh can save about 47.47 MTOE in agriculture sector and 34.96 MTOE from service sector. On the other hand, industry sector, which is accounted for 58% of the total energy consumption, failed to save energy, rather the country consumed 227 MTOE more energy than usual. The energy rebound effect that relies upon the activity effect and structural effect has also been estimated to examine the energy uses pattern of these sectors. The aggregate energy rebound effect was found to be 1480 MTOE, of which activity effect and structural effect contribute 91.21% and 8.78% respectively. DOI: http://dx.doi.org/10.3329/bjsir.v47i3.13066 Bangladesh J. Sci. Ind. Res. 47(3), 313-320 2012

scholarly journals Investment, technological progress and energy efficiency

2019 ◽  
Vol 19 (2) ◽  
Author(s):  
Antonia Díaz ◽  
Luis A. Puch
Keyword(s):  
Energy Saving ◽  
Technical Change ◽  
Energy Use ◽  
Aggregate Demand ◽  
Rebound Effect ◽  
Energy Prices ◽  
Capital Goods ◽  
Aggregate Effect ◽  
Energy Consuming ◽  
Aggregate Amount

Abstract In this paper we propose a theory to study how the aggregate demand of energy responds to energy prices and technical innovations that affect the price of energy services. In our theory, energy use is determined by the interaction of the choice of Energy Saving Technical Change with energy prices and Investment Specific Technical Change (ISTC). The key mechanism is that the energy saving technology is embodied in capital vintages as a requirement that determines their energy intensity. We show that higher ISTC that increases the quality of capital goods is an energy saving device and, therefore, a substitute for Energy Saving Technical Change (ESTC). However, higher ISTC that rises the efficiency in producing capital goods is energy consuming and fosters ESTC to compensate for the amount of energy required by the new investment. A higher price of energy also induces a higher level of ESTC, but the aggregate amount of energy used may not be affected as investment does not change. These effects are amplified with rising prices of energy. Thus, our theory can be used to test when and how we should see a rebound effect in energy use at the aggregate level and to evaluate the aggregate effect of any policy aiming to reduce energy use.

scholarly journals Finding Resilient and Energy-saving Control Strategies in Smart Homes

2016 ◽  
Vol 83 ◽  
pp. 976-981 ◽  
Author(s):  
Stefano Marrone ◽  
Ugo Gentile
Keyword(s):  
Energy Saving ◽  
Control Strategies ◽  
Smart Homes ◽  
Energy Saving Control

scholarly journals Smart Demand Response Based on Smart Homes

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Jingang Lai ◽  
Hong Zhou ◽  
Wenshan Hu ◽  
Dongguo Zhou ◽  
Liang Zhong
Keyword(s):  
Smart Grid ◽  
Energy Saving ◽  
Demand Response ◽  
Energy Savings ◽  
Supply And Demand ◽  
Smart Homes ◽  
Electricity Supply ◽  
Power Cable ◽  
Peak Shaving ◽  
Energy Feedback

Smart homes (SHs) are crucial parts for demand response management (DRM) of smart grid (SG). The aim of SHs based demand response (DR) is to provide a flexible two-way energy feedback whilst (or shortly after) the consumption occurs. It can potentially persuade end-users to achieve energy saving and cooperate with the electricity producer or supplier to maintain balance between the electricity supply and demand through the method of peak shaving and valley filling. However, existing solutions are challenged by the lack of consideration between the wide application of fiber power cable to the home (FPCTTH) and related users’ behaviors. Based on the new network infrastructure, the design and development of smart DR systems based on SHs are related with not only functionalities as security, convenience, and comfort, but also energy savings. A new multirouting protocol based on Kruskal’s algorithm is designed for the reliability and safety of the SHs distribution network. The benefits of FPCTTH-based SHs are summarized at the end of the paper.

Energy saving in smart homes based on consumer behavior: A case study

2015 ◽  
Author(s):  
Michael Zehnder ◽  
Holger Wache ◽  
Hans-Friedrich Witschel ◽  
Danilo Zanatta ◽  
Miguel Rodriguez
Keyword(s):  
Consumer Behavior ◽  
Energy Saving ◽  
Smart Homes
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