A Practical Approach to Launch the Low-Cost Monitoring Platforms for Nearly Net-Zero Energy Buildings in Vietnam

Abstract

Buildings with solar rooftops have become vital objects in the energy transition in Vietnam. In this context, the demand for research on energy management solutions to use energy efficiently and increase PV energy absorption capacity is rising. In this paper, we present a practical route to developing a low-cost monitoring platform to meet the building energy management in the country. First, our project built a monitoring architecture with high-density wireless sensors in an office building in Vietnam. Next, we discussed the influence of significant obstacles such as technical issues, users, and cost on the resilience and reliability of the monitoring system. Then, we proposed essential solutions for data quality improvement by testing sensors, detecting wireless sensor network errors, and compensating for data losses by embedding machine learning. We found the platform’s potential in developing a rich database of building characteristics and occupants. Finally, we proposed plans exploiting the data to reduce wasted energy in equipment operation, change user behaviors, and increase auto-consumption PV power. The effectiveness of the monitoring platform was an approximate 62% energy reduction in the first year. The results are a cornerstone for implementing advanced research as modeling and real-time optimal control toward nearly zero-energy buildings.

Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network

“…For example, a study in [21] shows the importance of input features in predictive models. The data structure combines the fluctuation components such as weather by seasons and human activities by the time [25,26], which could give a more robust model. Therefore, adding the correlation variables to the model and more data for model training may need to be considered.…”