scholarly journals INFO Sheet A17: Reference single family solar domestic hot water system for France

2017 ◽  
Author(s):  
Daniel Mugnier
Keyword(s):  
Water System ◽  
Hot Water ◽  
Single Family ◽  
Domestic Hot Water

scholarly journals Domestic hot water system in residential buildings: production, distribution and consumption energy loss. Monitoring campaign in two Danish detached houses

2021 ◽  
Vol 2069 (1) ◽  
pp. 012104
Author(s):  
Anna Marszal-Pomianowska ◽  
Rasmus Lund Jensen ◽  
Michal Pomianowski ◽  
Olena Kalyanova Larsen ◽  
Scharling Jacob Jørgensen ◽  
...  
Keyword(s):  
Total Energy ◽  
Energy Use ◽  
Residential Buildings ◽  
Water System ◽  
District Heating ◽  
Hot Water ◽  
Single Family ◽  
Total Energy Consumption ◽  
Domestic Hot Water ◽  
Building Typology

Abstract The share of the energy use for domestic hot water (DHW) in the total energy consumption of buildings is becoming more and more prominent. Depending on the building typology it varies between 20% to 50% of the total energy usage for old and new built single family house, respectively. The aim of this paper is to determine the energy losses in the DHW installation with division between: a) loss at the production point, b) loss in the distribution, and c) loss at the draw-off points using the results of the measurements of DHW consumption in two single family houses connected to district heating grid. The total Eloss for the two houses vary between 17% and 26%. For House 1, the production loss accounts for 8%, the pipe loss for 15% and loss at the draw off points for 3%. Moreover, the results shown that the layout of the house, in particular the placement of the bathrooms with showers or bath tubs has significant impact on the size of the distribution losses.

Simulation of a combined heating, cooling and domestic hot water system based on ground source absorption heat pump

2014 ◽  
Vol 126 ◽  
pp. 113-122 ◽  
Author(s):  
Wei Wu ◽  
Tian You ◽  
Baolong Wang ◽  
Wenxing Shi ◽  
Xianting Li
Keyword(s):  
Heat Pump ◽  
Water System ◽  
Hot Water ◽  
Absorption Heat Pump ◽  
Domestic Hot Water ◽  
Ground Source

scholarly journals Analysis and Adaptation of Q-Learning Algorithm to Expert Controls of a Solar Domestic Hot Water System

2019 ◽  
Vol 2 (2) ◽  
pp. 15 ◽  
Author(s):  
Bettoni ◽  
Soppelsa ◽  
Fedrizzi ◽  
del Toro Matamoros
Keyword(s):  
Control Algorithm ◽  
Fuzzy Controller ◽  
Learning Algorithm ◽  
Water System ◽  
Hot Water ◽  
Reference Case ◽  
Domestic Hot Water ◽  
Q Learning ◽  
Performance Factor ◽  
Seasonal Performance

This paper discusses the development of a coupled Q-learning/fuzzy control algorithm to be applied to the control of solar domestic hot water systems. The controller brings the benefit of showing performance in line with the best reference controllers without the need for devoting time to modelling and simulations to tune its parameters before deployment. The performance of the proposed control algorithm was analysed in detail concerning the input membership function defining the fuzzy controller. The algorithm was compared to four standard reference control cases using three performance figures: the seasonal performance factor of the solar collectors, the seasonal performance factor of the system and the number of on/off cycles of the primary circulator. The work shows that the reinforced learning controller can find the best performing fuzzy controller within a family of controllers. It also shows how to increase the speed of the learning process by loading the controller with partial pre-existing information. The new controller performed significantly better than the best reference case with regard to the collectors’ performance factor (between 15% and 115%), and at the same time, to the number of on/off cycles of the primary circulator (1.2 per day down from 30 per day). Regarding the domestic hot water performance factor, the new controller performed about 11% worse than the best reference controller but greatly improved its on/off cycle figure (425 from 11,046). The decrease in performance was due to the choice of reward function, which was not selected for that purpose and it was blind to some of the factors influencing the system performance factor.

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