scholarly journals Economic, social and environmental impact analysis of an indigenously developed energy optimization system for German office and residential building types

2021 ◽  
Author(s):  
K Umut Gökçe ◽  
H Ufuk Gökçe
Keyword(s):  
Energy Efficiency ◽  
Resource Use ◽  
Social Impact ◽  
Lifetime Cost ◽  
Energy Optimization ◽  
Residential Building ◽  
Building Energy ◽  
Environmental Indicators ◽  
Optimization System ◽  
Building Energy Optimization

Abstract This work addresses the economic, social and environmental analysis of an indigenously developed building energy optimization system for residential and office buildings in Germany. The developed system consists of 14 different wireless network embedded sensors, wireless communication protocol, multi-dimensional data warehouse, graphical user interfaces and energy optimization software with artificial intelligence-backed control algorithms. The R&D activity is accomplished in the ‘Intelligent Building Energy Management System’ research project, which is funded by the State of Lower Saxony—Germany in the frame of Innovation Support Program between the years 2014 and 2018. The system is tested in two appropriately selected test buildings in Germany. It has been recorded that the system provides energy efficiency levels between 29.34% and 38.18% under different seasonal and occupancy conditions in office and residential building types. The analysis is accomplished within the framework of four main indicators. These are economic indicators including calculations of lifetime cost and payback rate methods, resource use indicators including the calculation of reduced energy use as a result of the energy efficiency provided by the system, social indicator including additional expendable income calculations and environmental indicators including calculations depicting the benefits of reducing harmful emissions. Analysis results illustrate that building energy optimization systems provide a cost-effective method for promoting energy efficiency goals. Results of the indicators prove monetary benefits for the building occupants in terms of resource use, return on investment and social impact, as well as quantifiable benefits for easing the harmful effects of climate change phenomenon.

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.

Molecule building, energy optimization and graphics representations of molecules

1991 ◽  
Vol 9 (1) ◽  
pp. 47
Author(s):  
F.S. Ganda-Kesuma ◽  
K.J. Miller ◽  
D. Rubenstein ◽  
M.J. Bean ◽  
J.H. Hellman
Keyword(s):  
Energy Optimization ◽  
Building Energy ◽  
Building Energy Optimization

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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