Data play an essential role in the optimal control of smart buildings’ operation, especially in building energy-management for the target of nearly zero buildings. The building monitoring system is in charge of collecting and managing building data. However, device imperfections and failures of the monitoring system are likely to produce low-quality data, such as data loss and inconsistent data, which then seriously affect the control quality of the buildings. This paper proposes a new approach based on Gaussian process regression for data-quality monitoring and sensor network data compensation in smart buildings. The proposed method is proven to effectively detect and compensate for low-quality data thanks to the application of data analysis to the energy management monitoring system of a building model in Viet Nam. The research results provide a good opportunity to improve the efficiency of building energy-management systems and support the development of low-cost smart buildings.

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.

Building is one of the most consuming sector with 5 MWh per person per year. Building energy efficiency is already addressed by countries policies but is still a topic of research. In our paper, we are addressing a new way of improving energy consumption in building, based on Internet of Things (IoT). IoT is synonymous of increase of the amount and accessibility of measurements. How is this impressive growth possible? How to use these new data to reduce building energy consumption and bring new energy performance services? IoT technologies are analysed and future tendencies are proposed. Our analysis is illustrated on the experience acquired within 2 smart building research and teaching platforms, one in Grenoble France, and the other in Hanoi, Vietnam. These research platform aim at improving energy efficiency and sobriety, and prepare building to energy grid services.

A significant solution to achieve energy efficiencies and stability of the electrical network is upgrading auto-consumption in a single PV system. In this paper, we presented a methodology to implement low-cost sensors and actuators to have better monitoring and control for a feasibility solution. This approach has been conducted through a case study on Greenhouse’s PV system in Grenoble, France. We proposed a minimal set of sensors to reduce system complexity while giving us enough information to make decisions. Some technical issues were analyzed as accuracy, sampling rate, response-ability of our system. The influences of inverter operation mode to system losses were considered. After that, we figured out energy problematics in our system based on available design data and simulation data from PVSyst. A PV production prediction model was investigated with input as forecast cloudiness data collected from web service, updated every 3 hours. This model, combined with real-time monitoring data and setting mode on an off-grid inverter, was used to identify control strategies with objectives of avoiding oversize storage capacity and maximizing the autonomous duration of the PV system.