Practice for Installation and Service of Solar Space Heating Systems for One- and Two-Family Dwellings

10.1520/e0683 ◽  
2008 ◽  
Author(s):  
Keyword(s):  
Space Heating ◽  
Heating Systems

Solar space heating with air and liquid systems

1980 ◽  
Vol 295 (1414) ◽  
pp. 349-359
Keyword(s):  
Energy Savings ◽  
Solar Heating ◽  
Performance Data ◽  
Space Heating ◽  
Cooling Systems ◽  
Heating Systems ◽  
Fuel Prices ◽  
Heating And Cooling ◽  
Liquid Systems ◽  
And Storage

Seasonal and annual performance data are available on only a limited number of the several thousand solar space heating systems now in operation. The emerging information indicates that most of the heat required in buildings can be supplied by solar energy delivered from flat-plate collectors and stored overnight in tanks of water and bins of rock pebbles. Numerous mechanical and operational problems, mainly in liquid collection and storage systems, demand attention. Annual costs of solar heating equipment and its installation usually exceed current values of energy savings, but fuel prices are expected to escalate at rates which often favour solar purchase today. Detailed performance data on several types of solar heating and cooling systems in buildings of identical design are presented, compared and interpreted. Maintenance and repair requirements are noted and contrasted, and forecasts of use in various applications are presented.

A Theoretical Study on Decentralized Space Heating System

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Zhi-Ping Song
Keyword(s):  
Energy Conservation ◽  
Total Energy ◽  
Global Climate ◽  
Energy System ◽  
Specific Consumption ◽  
Space Heating ◽  
Full Potential ◽  
Heating Systems ◽  
End Use ◽  
The Impact

Global climate change mitigation requires the fossil fuel consumption substantially reduced. Space heating is an energy-consuming sector. Despite the fact that the thermal efficiency of current space heating systems has achieved a value higher than 85%, corresponding to lower than 40kg c.e./GJ, there is still a big potential for energy conservation. In order to realize the full potential, investigations of heating systems should appeal to reversibility/exergy analysis made on total energy concept basis. This paper starts with an introduction of the concept “reversible mode of heating,” leading the readers think of space heating in terms of reversibility. Right after, a systematic reversibility analysis on a “mine to home” basis is conducted to reveal the impact of any irreversibility of all subsystems or devices involved in the total energy system of heating on the fuel/monetary specific consumption of unit end-use heat. The paper points out that although combined heat and power (CHP) and electrically driven heat pump are both of “reversible mode,” the former is far more favorable in terms of energy conservation. The recently ascent decentralized energy system provides the best circumstances for CHP implementation. The demand-side improvement is a topic of most importance but frequently neglected. This study reveals that, if properly engineered, this improvement together with adopting a direct type of heat grid might lower the fuel specific consumption of end-use heat of CHP to a level as low as 13–9kg c.e./GJ.

scholarly journals Analyzing the Effects of the Dwelling Characteristics on Space Heating Costs with the Rank Correlation Bayesian Network Model

2021 ◽  
Author(s):  
Volkan Sevinç
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Network Model ◽  
Building Materials ◽  
Structure Learning ◽  
Rank Correlation ◽  
Space Heating ◽  
Bayesian Network Model ◽  
Heating Systems ◽  
External Wall

Abstract Energy is one of the main concerns of humanity because energy resources are limited and costly. In order to reduce the costs and to use the energy for space heating effectively, new building materials, techniques and insulations facilities are being developed. Therefore, it is important to know which factors affect the space heating costs. This study aims to introduce the novel Rank Correlation Bayesian Network model and its application in analyzing the effects of dwelling characteristics on the space heating costs. The results show that the constructed Rank Correlation Bayesian Network model performed better than the Bayesian networks models estimated by Bayesian search, PC and Greedy Thick Thinning algorithms, which are kinds of structure learning algorithms having different kinds of estimation mechanisms to build Bayesian networks. The constructed Rank Correlation Bayesian Network model shows that the space heating costs of the dwellings are mostly affected by the heating systems used. Coal stoves, air conditioners and electric stoves appear to be the costliest heating systems. The second most important factor appears to be the existence of external wall insulation. The lack of external wall insulation almost doubles the space heating costs. The third most important factor is the building age. Dwellings on the ground floors and the first floors appear to pay the highest space heating costs. Therefore, dwellings on these floors need to be more effectively insulated. As the size of the dwelling increases the heating cost increases too. Another result is that facing directions and floor levels of the dwellings have the least effects on their space heating.

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