A deep Q-network (DQN) was applied for model-free optimal control balancing between different HVAC systems. The DQN was coupled to a reference office building: an EnergyPlus simulation model provided by the U.S. Department of Energy. The building was air-conditioned with four air-handling units (AHUs), two electric chillers, a cooling tower, and two pumps. EnergyPlus simulation results for eleven days (July 1-11) and three subsequent days (July 12-14) were used to improve the DQN policy and test the optimal control. The optimization goal was to minimize the building's energy use while maintaining the indoor CO 2 concentration below 1,000 ppm. It was revealed that the DQN-a reinforcement learning method-can improve its control policy based on prior actions, states, and rewards. The DQN lowered the total energy usage by 15.7% in comparison with the baseline operation while maintaining the indoor CO 2 concentration below 1,000 ppm. Compared to model predictive control, the DQN does not require a simulation model, or a predetermined prediction horizon, thus delivering model-free optimal control. Furthermore, it was demonstrated that the DQN can find balanced control actions between different energy consumers in the building, such as chillers, pumps, and AHUs.
The purpose of the present study was to investigate the relevance of building thermal performance and characteristics to building energy consumption. This paper reports an energy analysis of 4625 office buildings in Seoul, South Korea, using data from the Korean national building energy database and architectural database. The following four research questions were investigated: (1) Do old buildings consume more energy than new ones? (2) Have strict prescriptive building energy codes contributed to the reduction in energy use intensity (EUI, kWh/m2·year) over the past several decades? (3) What are the characteristics of building energy consumption in terms of season, age, and cooling system (electric chiller vs absorption chiller)? (4) Which factors in the Korean building energy database are relevant to building energy consumption? The analyses revealed that, contrary to common assumptions, new buildings did not always consume less energy than old buildings, and it may be wrong to attribute intensification of prescriptive building energy codes directly to building energy efficiency improvements. In addition, the building characteristics (i.e., district, year built, number of floors, number of elevators, and total floor area) available in the Korean building energy database do not adequately explain building energy consumption, and the existing data collection method needs further improvement.
This paper presents a simulation study to reduce heating and cooling energy demand of a school building in Seoul Metropolitan Area, Korea. The aim of this study was to estimate the impact of passive vs. active approaches on energy savings in buildings using EnergyPlus simulation. By controlling lighting, the energy saving of the original school building design was found most significant, and increased by 32% when the design was improved. It is noteworthy that energy saving potential of each room varies significantly depending on the rooms' thermal characteristics and orientation. Thus, the analysis of energy saving should be introduced at the individual space level, not at the whole building level. Additionally, the simulation studies should be involved for rational decision-making. Finally, it was concluded that priority should be given to passive building design strategies, such as building orientation, as well as control and utilization of solar radiation. These passive energy saving strategies are related to urban, architectural design, and engineering issues, and are more beneficial in terms of energy savings than active strategies.
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