CHP sizing and domestic building energy cost optimization

2016 ◽  
Author(s):  
Dongmin Yu ◽  
Huiming Zhang ◽  
Da Huo ◽  
Simon Le Blond
Keyword(s):  
Energy Cost ◽  
Cost Optimization ◽  
Building Energy

Cost Optimization of Net-Zero Energy House

2007 ◽  
Author(s):  
George A. Mertz ◽  
Gregory S. Raffio ◽  
Kelly Kissock
Keyword(s):  
Energy Use ◽  
Cost Optimization ◽  
Building Design ◽  
Building Energy ◽  
Zero Energy ◽  
Resource Limitations ◽  
Net Zero Energy ◽  
Net Zero ◽  
Building Energy Use ◽  
The Cost

Environmental and resource limitations provide increased motivation for design of net-zero energy or net-zero CO2 buildings. The optimum building design will have the lowest lifecycle cost. This paper describes a method of performing and comparing lifecycle costs for standard, CO2-neutral and net-zero energy buildings. Costs of source energy are calculated based on the cost of photovoltaic systems, tradable renewable certificates, CO2 credits and conventional energy. Building energy simulation is used to determine building energy use. A case study is conducted on a proposed net-zero energy house. The paper identifies the least-cost net-zero energy house, the least-cost CO2 neutral house, and the overall least-cost house. The methodology can be generalized to different climates and buildings. The method and results may be of interest to builders, developers, city planners, or organizations managing multiple buildings.

scholarly journals Green Building Energy Cost Optimization With Deep Belief Network and Firefly Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Yan Liao ◽  
Yong Liu ◽  
Chaoyu Chen ◽  
Lili Zhang
Keyword(s):  
Energy Cost ◽  
Firefly Algorithm ◽  
Cost Optimization ◽  
Green Building ◽  
Central Business District ◽  
Multi Objective Optimization ◽  
Commercial Building ◽  
Multi Objective ◽  
Optimization Framework ◽  
Business District

In this research, we propose a multi-objective optimization framework to minimize the energy cost while maintain the indoor air quality. The proposed framework is consisted with two stages: predictive modeling stage and multi-objective optimization stage. In the first stage, artificial neural networks are applied to predict the energy utility in real-time. In the second stage, an optimization algorithm namely firefly algorithm is utilized to reduce the energy cost while maintaining the required IAQ conditions. Industrial data collected from a commercial building in central business district in Chengdu, China is utilized in this study. The results produced by the optimization framework show that this strategy reduces energy cost by optimizing operations within the HAVC system.

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