scholarly journals Diffusion Dynamics of Energy Service Companies in the Residential Sector

2021 ◽  
Vol 15 ◽  
pp. 8-15
Author(s):  
Andra Blumberga ◽  
Dagnija Blumberga ◽  
Gatis Žogla ◽  
Claudio Rochas ◽  
Marika Rošā ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
System Dynamics ◽  
Residential Building ◽  
Building Energy ◽  
Building Energy Efficiency ◽  
Residential Sector ◽  
Service Companies ◽  
Diffusion Dynamics ◽  
Energy Service ◽  
End Use

Energy service contracting (EPC) is one of the private sector instruments to improve end use energy efficiency. The dominant part of EPC projects is implemented in the industrial, governmental and municipal sectors while the residential sector has been less attractive to energy service companies (ESCO). This paper describes the first EPC project implemented in a multi apartment building in Latvia. A combination of this project experience, the system dynamics modelling, and microeconomics theory has been used to develop the system dynamics model to capture the interactions and feedback from the ESCO market for residential building energy efficiency. Three policy tools were tested for use in improving the performance of the system. The purpose of the tests was to facilitate the diffusion (or distribution) of EPC in the residential sector.

scholarly journals Estimation of Building Energy Efficiency Performance Using Radial Basis Function Neural Network

2018 ◽  
Vol 7 (4.35) ◽  
pp. 755 ◽  
Author(s):  
Che Munira Che Razali ◽  
Shamsul Faisal Mohd Hussein ◽  
Nolia Harudin ◽  
Shahrum Shah Abdullah
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Radial Basis Function ◽  
Basis Function ◽  
Residential Building ◽  
Building Energy ◽  
Estimation Methods ◽  
Building Energy Efficiency ◽  
Radial Basis ◽  
Efficiency Performance

Since a pass few decades up to recent, building energy efficiency performance is the top priority due to the sustainability of energy and quality of life. According to recent study related to computer experiment, there are various types of the model has been proposed by the researcher to improve the performance of building energy efficiency. However, there is no empirical evidence to prove the best method in prediction and estimation of energy efficiency that ensure adequate energy to meet todays and future needs. The objective of this paper is to propose Radial Basis Function Neural Network (RBFNN) for estimating the heating load and cooling load of a residential building. This study set out to evaluate different estimation methods of residential building energy efficiency using RBFNN. The data of residential building are obtained from UCI Machine Learning Repository. The dataset of simulation using Ecotect consists of 768 samples with 8 input features and 2 output variables were used to train and test the algorithm of RBFNN. The input variables involved in this experiment are relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a building, while the output variables are heating and cooling loads of the building. The analytical result of the proposed method shows that RBFNN produces better result and performance compared with the previous researches.

Ningbo City’s Residential Building Energy Efficiency Evaluation Index System

2011 ◽  
Vol 374-377 ◽  
pp. 150-154
Author(s):  
Jing Yu ◽  
Rong Yue Zheng ◽  
Joseph Huang ◽  
Ying Chen
Keyword(s):  
Energy Efficiency ◽  
Index System ◽  
Residential Building ◽  
Relevant Information ◽  
Building Energy ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Efficiency Evaluation ◽  
Building Energy Efficiency ◽  
Energy Efficiency Evaluation

According to the actual situation in Ningbo, combined with both relevant information from both China's current building energy efficiency standards and that of foreign building's energy efficiency and environmental assessment methods, together established the Ningbo City residential building energy efficiency evaluation index system. Using the AHP application to evaluate, analyze and calculate to determine the weight of the evaluation index.

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