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Longwave radiative heat transfer is a key determinant of energy consumption in buildings
and view factor calculations are therefore required for the detailed simulation of heat transfer
between buildings and their environment as well as for heat exchange within rooms. Typically,
these calculations are either derived through analytical means or performed as a part of the simulation
process. This paper describes the methodology for employing RADIANCE, a command-line
open-source raytracing software, for performing view factor calculations. Since it was introduced
in the late-1980s, RADIANCE has been almost exclusively employed as the back-end engine for
lighting simulations. We discuss the theoretical basis for calculating view factors through Monte
Carlo calculations with RADIANCE and propose a corresponding workflow. The results generated
through RADIANCE are validated by comparing them with analytical solutions. The fundamental
methodology proposed in this paper can be scaled up to calculate view factors for more complex,
practical scenarios. Furthermore, the portability, multi-processing functionality and cross-platform
compatibility offered by RADIANCE can also be employed in the calculation of view factors.
Climate-Based Analysis for the Potential Use of Coconut Oil as Phase Change Material in Buildings
(2021)
One of the most efficient measures to reduce energy consumption in buildings is using
passive thermal comfort strategies. This paper shows the potential of coconut oil as a bio-based
phase change material (PCM) incorporated into construction components to improve the thermal
performance of buildings for several climates, due to its environmental advantages, wide availability,
and economic feasibility. The thermophysical properties of coconut oil were determined through
differential scanning calorimetry. Numerical simulations were conducted in ESP-r, comparing an
office space with a gypsum ceiling to one with coconut oil as PCM for 12 climate types in the Köppen–
Geiger classification. The results show that coconut oil is a suitable PCM for construction applications
under tropical and subtropical climates. This PCM can provide year-round benefits for these climates,
even though a higher melting point is needed for optimum performance during hotter months. The
highest demand reduction of 32% and a maximum temperature reduction of 3.7 °C were found in
Mansa, Zambia (Cwa climate). The best results occur when average outdoor temperatures are within
the temperature range of phase change. The higher the diurnal temperature range, the better the
results. Our findings contribute to a better understanding of coconut oil in terms of its properties
and potential for application in the building sector as PCM.
Daylight is important for the well-being of humans. Therefore, many office buildings use
large windows and glass facades to let more daylight into office spaces. However, this increases the
chance of glare in office spaces, which results in visual discomfort. Shading systems in buildings
can prevent glare but are not effectively adapted to changing sky conditions and sun position,
thus losing valuable daylight. Moreover, many shading systems are also aesthetically unappealing.
Electrochromic (EC) glass in this regard might be a better alternative, due to its light transmission
properties that can be altered when a voltage is applied. EC glass facilitates zoning and also supports
control of each zone separately. This allows the right amount of daylight at any time of the day.
However, an effective control strategy is still required to efficiently control EC glass. Reinforcement
learning (RL) is a promising control strategy that can learn from rewards and penalties and use this
feedback to adapt to user inputs. We trained a Deep Q learning (DQN) agent on a set of weather data
and visual comfort data, where the agent tries to adapt to the occupant’s feedback while observing
the sun position and radiation at given intervals. The trained DQN agent can avoid bright daylight
and glare scenarios in 97% of the cases and increases the amount of useful daylight up to 90%, thus
significantly reducing the need for artificial lighting.
Thermal comfort is one of the most important factors for occupant satisfaction and, as a result, for the building energy performance. Decentralized heating and cooling systems, also known as “Personal Environmental Comfort Systems” (PECS), have attracted significant interest in research and industry in recent years. While building simulation software is used in practice to improve the energy performance of buildings, most building simulation applications use the PMV approach for comfort calculations. This article presents a newly developed building controller that uses a holistic approach in the consideration of PECS within the framework of the building simulation software Esp-r. With PhySCo, a dynamic physiology, sensation, and comfort model, the presented building controller can adjust the setpoint temperatures of the central HVAC system as well as control the use of PECS based on the thermal sensation and comfort values of a virtual human. An adaptive building controller with a wide dead-band and adaptive setpoints between 18 to 26 °C (30 °C) was compared to a basic controller with a fixed and narrow setpoint range between 21 to 24 °C. The simulations were conducted for temperate western European climate (Mannheim, Germany), classified as Cfb climate according to Köppen-Geiger. With the adaptive controller, a 12.5% reduction in end-use energy was achieved in winter. For summer conditions, a variation between the adaptive controller, an office chair with a cooling function, and a fan increased the upper setpoint temperature to 30 °C while still maintaining comfortable conditions and reducing the end-use energy by 15.3%. In spring, the same variation led to a 9.3% reduction in the final energy. The combinations of other systems were studied with the newly presented controller.
Solar radiation data is essential for the development of many solar energy applications ranging from thermal collectors to building simulation tools, but its availability is limited, especially the diffuse radiation component. There are several studies aimed at predicting this value, but very few studies cover the generalizability of such models on varying climates. Our study investigates how well these models generalize and also show how to enhance their generalizability on different climates. Since machine learning approaches are known to generalize well, we apply them to truly understand how well they perform on different climates than they are originally trained. Therefore, we trained them on datasets from the U.S. and tested on several European climates. The machine learning model that is developed for U.S. climates not only showed low mean absolute error (MAE) of 23 W/m2, but also generalized very well on European climates with MAE in the range of 20 to 27 W/m2. Further investigation into the factors influencing the generalizability revealed that careful selection of the training data can improve the results significantly