This research presents a simulation method that can evaluate the annual energy consumption of lighting and HVAC systems for buildings where daylight control is used. A building thermal simulation program, BEST is coupled with a lighting simulation program Radiance by using a newly developed program for linking the two programs. In this method, annual simulations of daylight utilization control are performed prior to annual thermal load and HVAC energy simulations and flexible combinations with other lighting simulation programs are allowed. We also conducted a parameter study and analyzed the effects of factors such as types of window glass and blind conditions on primary energy consumption of lighting and HVAC systems.
In the near future, it is possible that a smart grid real-time pricing (RTP) scheme will be introduced. In the RTP, the unit price of electricity will be changed frequently, such as at every 10-min interval by several to ten times. When building-multi air-conditioner's operation is planned so as to suppress the electricity fee according to RTP, it is desirable to predict the occurrence of maintenance control, which is a typical protection embedded control and consumes approximately full power. However, it appears to be an abrupt disturbance from the RTP control system's point of view. In this research, we propose a prediction model of maintenance operation during the RTP adaptive control. The model was implemented as a six-layer perceptron neural network which inputs 840 air-conditioner operation histories in parallel. Simulation experiments were conducted to evaluate the prediction accuracy and the improvement the RTP control. In order to verify the effectiveness of this prediction model, RTP adaptive control score was verified by computer simulation. As a result, the following knowledge was obtained. (1)The prediction precision (Accuracy) of this six-layer deep learning NN maintenance model was 94%. (2)When using this model for RTP control, the result that the electricity charge is improved by about 20% was obtained. These results show the possibility that the RTP adaptive control can be improved to a significant level by predicting the maintenance operation which has been regarded as a sudden disturbance by the RTP adaptive control system so far.
Recently, a number of buildings measuring electricity consumption using a smart meter has increased significantly, and electricity consumption data at every 30 minutes is available in such buildings. However, because the data show only the total electricity consumption for each building, the amount of electricity used for each end use is unclear. In this study, smart meter data analysis and building simulation was performed for 200 campus buildings in Osaka University with the aim to develop an effective usage of smart meter data. Electricity consumption data for 200 campus buildings provided by the university’s “electricity power measuring system” was used for this study. These data were disaggregated into three primary components “base load,” “activity load,” and “air-conditioning load” using regression analysis. Not surprisingly, the analysis of the disaggregated data indicated that the electricity consumption of the buildings that had been recently renovated tended to decrease. Therefore, the heat load reduction effect by building retrofit was validated by building simulation (EnergyPlus). Then, highly useful methodology to generate the schedules for heating, ventilation, and air conditioning system simulation using the disaggregated data was developed. As a result, it was found that roughly 50% of the air-conditioning load in February decreased by insulation retrofit and installation of air-to-air total heat exchanger. Based on this, the energy-saving potential through building retrofit in the whole campus was estimated using the campus energy demand estimation model. This model was developed by Archetype Engineering Bottom-up Modeling. Nine cases of building retrofit were set for this model, and it was found that 26.4% of electricity demand can be reduced through building retrofit in the whole campus.
For addressing the drought situation of large cities, rainwater is utilized for toilets, etc. In residential areas, it is already being used for watering and spraying plants using rainwater tanks. For office buildings and the like, rainwater is collected from the rooftop, and stored in the basement and used in 2150 places. Although there have been reports on design methods and processing performance up until now, little research on environmental load has been performed. Therefore, three kinds of flow sheets were selected, and CO2 emissions for treating rainwater and through replenishing tap water were calculated. It became clear that it is possible to reduce the environmental burden by increasing the water supply substitute rate and selecting proper equipment.