Person:

Malkawi, Ali

Natural ventilation (NV) is a key sustainable solution for reducing the energy use in buildings, improving thermal comfort, and maintaining a healthy indoor environment. However, the energy savings and environmental benefits are affected greatly by ambient air pollution in China. Here we estimate the NV potential of all major Chinese cities based on weather, ambient air quality, building configuration, and newly constructed square footage of office buildings in the year of 2015. In general, little NV potential is observed in northern China during the winter and southern China during the summer. Kunming located in the Southwest China is the most weather-favorable city for natural ventilation, and reveals almost zero loss due to air pollution. Building Energy Simulation (BES) is conducted to estimate the energy savings of natural ventilation in which ambient air pollution and total square footage must be taken into account. Beijing, the capital city, displays limited per-square-meter saving potential due to the unfavorable weather and air quality for natural ventilation, but its largest total square footage of office buildings makes it become the city with the greatest energy saving opportunity in China. Our analysis shows that the aggregated energy savings potential of office buildings at 35 major Chinese cities is 112 GWh in 2015, even after allowing for a 43 GWh loss due to China’s serious air pollution issue especially in North China. 8–78% of the cooling energy consumption can be potentially reduced by natural ventilation depending on local weather and air quality. The findings here provide guidelines for improving current energy and environmental policies in China, and a direction for reforming building codes.

Although natural ventilation is applicable to most buildings, architects and engineers today struggle to integrate it as an alternative to mechanical ventilation systems due to its uncertainty. This paper presents the application of regression algorithms and identification methods to single-sided natural ventilation with two opening (SS2). The result of this work provides a predictive control-oriented model which co-optimize the CO2 level and thermal comfort for natural ventilation with SS2 configuration.

Natural ventilation is a promising passive technology to improve building energy performance and indoor air quality. However, the control of natural ventilation is a challenge in building technology and is often missing in building system advanced control design. This paper proposes the application of Model Predictive Control (MPC) and Reinforcement Learning (RL) control for winter natural ventilation control and evaluates both through on-site control experiments. Furthermore, this paper suggests the internal connection between MPC and RL control as simulation-based control, by transforming the MPC design into RL control design. This paper also highlights the RL control as a potential solver-free solution in deployment for building systems.

The thermally activated building system (TABS) is becoming a popular heating option in buildings in the US. A typical TABS heating control strategy uses an embedded slab temperature sensor or room air temperature sensor as the control reference. TABS control may be disturbed by heat gain in the room space, for example, the intense solar radiation, due to the thermal inertia of the thermal mass slab. Overheating is often observed in the south-facing offices in winter. The fundamental problem is that the TABS heating control design is only at the system level, not considering the whole building and the built environment. This paper proposes a novel data-driven control strategy to augment the existing TABS heating control strategy considering future weather conditions. The TABS and solar energy are modeled in a monolithic energy model for the first time through a data-driven method. Furthermore, this data-driven control strategy has been successfully evaluated in a smart building in Cambridge, Massachusetts.