Whole-building Energy Co-simulation for Dynamic Occupant-based Heating and Cooling Control with Rule-based and Q-learning Algorithms

Author(s):  
Tongrui An
Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1080
Author(s):  
Mamdooh Alwetaishi ◽  
Omrane Benjeddou

The concern regarding local responsive building design has gained more attention globally as of late. This is due to the issue of the rapid increase in energy consumption in buildings for the purpose of heating and cooling. This has become a crucial issue in educational buildings and especially in schools. The major issue in school buildings in Saudi Arabia is that they are a form of prototype school building design (PSBD). As a result, if there is any concern in the design stage and in relation to the selection of building materials, this will spread throughout the region. In addition to that, the design is repeated regardless of the climate variation within the kingdom of Saudi Arabia. This research will focus on the influence of the window to wall ratio on the energy load in various orientations and different climatic regions. The research will use the energy computer tool TAS Environmental Design Solution Limited (EDSL) to calculate the energy load as well as solar gain. During the visit to the sample schools, a globe thermometer will be used to monitor the globe temperature in the classrooms. This research introduces a framework to assist architects and engineers in selecting the proper window to wall ratio (WWR) in each direction within the same building based on adequate natural light with a minimum reliance on energy load. For ultimate WWR for energy performance and daylight, the WWR should range from 20% to 30%, depending on orientation, in order to provide the optimal daylight factor combined with building energy efficiency. This ratio can be slightly greater in higher altitude locations.


Author(s):  
Heangwoo Lee ◽  
Xiaolong Zhao ◽  
Janghoo Seo

Recent studies on light shelves found that building energy efficiency could be maximized by applying photovoltaic (PV) modules to light shelf reflectors. Although PV modules generate a substantial amount of heat and change the consumption of indoor heating and cooling energy, performance evaluations carried out thus far have not considered these factors. This study validated the effectiveness of PV module light shelves and determined optimal specifications while considering heating and cooling energy savings. A full-scale testbed was built to evaluate performance according to light shelf variables. The uniformity ratio was found to improve according to the light shelf angle value and decreased as the PV module installation area increased. It was determined that PV modules should be considered in the design of light shelves as their daylighting and concentration efficiency change according to their angles. PV modules installed on light shelves were also found to change the indoor cooling and heating environment; the degree of such change increased as the area of the PV module increased. Lastly, light shelf specifications for reducing building energy, including heating and cooling energy, were not found to apply to PV modules since PV modules on light shelf reflectors increase building energy consumption.


Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4805
Author(s):  
Shu Chen ◽  
Zhengen Ren ◽  
Zhi Tang ◽  
Xianrong Zhuo

Globally, buildings account for nearly 40% of the total primary energy consumption and are responsible for 20% of the total greenhouse gas emissions. Energy consumption in buildings is increasing with the increasing world population and improving standards of living. Current global warming conditions will inevitably impact building energy consumption. To address this issue, this report conducted a comprehensive study of the impact of climate change on residential building energy consumption. Using the methodology of morphing, the weather files were constructed based on the typical meteorological year (TMY) data and predicted data generated from eight typical global climate models (GCMs) for three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) from 2020 to 2100. It was found that the most severe situation would occur in scenario RCP8.5, where the increase in temperature will reach 4.5 °C in eastern Australia from 2080–2099, which is 1 °C higher than that in other climate zones. With the construction of predicted weather files in 83 climate zones all across Australia, ten climate zones (cities)—ranging from heating-dominated to cooling-dominated regions—were selected as representative climate zones to illustrate the impact of climate change on heating and cooling energy consumption. The quantitative change in the energy requirements for space heating and cooling, along with the star rating, was simulated for two representative detached houses using the AccuRate software. It could be concluded that the RCP scenarios significantly affect the energy loads, which is consistent with changes in the ambient temperature. The heating load decreases for all climate zones, while the cooling load increases. Most regions in Australia will increase their energy consumption due to rising temperatures; however, the energy requirements of Adelaide and Perth would not change significantly, where the space heating and cooling loads are balanced due to decreasing heating and increasing cooling costs in most scenarios. The energy load in bigger houses will change more than that in smaller houses. Furthermore, Brisbane is the most sensitive region in terms of relative space energy changes, and Townsville appears to be the most sensitive area in terms of star rating change in this study. The impact of climate change on space building energy consumption in different climate zones should be considered in future design strategies due to the decades-long lifespans of Australian residential houses.


2019 ◽  
Vol 10 (1) ◽  
pp. 3-16
Author(s):  
Claudia Schubert ◽  
Marc-Thorsten Hütt

Algorithms are the key instrument for the economy-on-demand using platforms for its clients, workers and self-employed. An effective legal enforcement must not be limited to the control of the outcome of the algorithm but should also focus on the algorithm itself. This article assesses the present capacities of computer science to control and certify rule-based and data-centric (machine learning) algorithms. It discusses the legal instruments for the control of algorithms and their enforcement and institutional pre-conditions. It favours a digital agency that concentrates expertise and bureaucracy for the certification and official calibration of algorithms and promotes an international approach to the regulation of legal standards.


2016 ◽  
Vol 819 ◽  
pp. 541-545 ◽  
Author(s):  
Sholahudin ◽  
Azimil Gani Alam ◽  
Chang In Baek ◽  
Hwataik Han

Energy consumption of buildings is increasing steadily and occupying approximately 30-40% of total energy use. It is important to predict heating and cooling loads of a building in the initial stage of design to find out optimal solutions among various design options, as well as in the operating stage after the building has been completed for energy efficient operation. In this paper, an artificial neural network model has been developed to predict heating and cooling loads of a building based on simulation data for building energy performance. The input variables include relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a building, and the output variables include heating load (HL) and cooling load (CL) of the building. The simulation data used for training are the data published in the literature for various 768 residential buildings. ANNs have a merit in estimating output values for given input values satisfactorily, but it has a limitation in acquiring the effects of input variables individually. In order to analyze the effects of the variables, we used a method for design of experiment and conducted ANOVA analysis. The sensitivities of individual variables have been investigated and the most energy efficient solution has been estimated under given conditions. Discussions are included in the paper regarding the variables affecting heating load and cooling load significantly and the effects on heating and cooling loads of residential buildings.


2011 ◽  
Vol 250-253 ◽  
pp. 3035-3038 ◽  
Author(s):  
Zheng Quan Liu ◽  
Yi Wang Bao

Building-integrated photovoltaics (BIPV) is a relatively recent new application of photovoltaic (PV) energy technologies whose energy output is affected by many design-related factors including PV module technologies, installation orientation, tilt and shadow range of solar panels. The shading analysis of a residential house’s PV roof in Beijing was conducted by using building analysis program Autodesk Ecotect 2010. Analysis result shows that there is no shadow on the PV roof from 9a.m to 4p.m in winter solstice when the solar altitude angle reaches minimum, which ensures almost no shading losses for the PV modules over the year. The differences in monthly energy output were compared in the case of different installation tilt of solar panels and PV module technologies. Finally, the contribution to the building energy of the PV roof was discussed. The results show that appropriate design and selection of PV modules can compensate for the energy requirements for building heating and cooling to some extent.


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