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.
Renewable electricity for off-grid areas is widely seen as one of the top choices in supporting local economic development in most countries, and so is Vietnam. Over the years, many isolated networks using renewable energy sources have been deployed for off-grid areas in Vietnam. However, the use of these energy sources in Vietnam’s isolated networks is still facing many challenges due to its infancy here. The issues of reliability and vulnerability of these networks are not given the expected attention. Another challenge is that the issues of the operational security of these systems could also be negatively affected by the variable nature of renewable sources, including static and dynamic security. For this reason, this study aims to contribute to a better understanding of integrating renewable energy into isolated networks, and in this case, using solar power for the An-Binh Island grid in Vietnam. The findings from this study suggest that choosing the right structure of the power mix could contribute to improving the operational security of isolated networks. Moreover, several solutions to enhance the reliability of this grid are also proposed. The NEPLAN environment was selected for simulation and analysis for all the scenarios in this study.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.