A taxonomy of data types and data collection methods for building energy monitoring and performance simulation

Abstract

Buildings contribute a major component of the world's total energy consumption. During the design phase, building energy simulation models can be used to predict building performance and design energy-efficient buildings that consume an optimal amount of energy, if operated and maintained within the confines of energy model assumptions. Similarly, Dynamic Data-Driven Simulation and Analysis (DDDSA) methods can be used during the operation and maintenance phase of buildings. Such methods allow models to be seeded periodically with real-time data that influences the simulation system for better analysis and prediction of system performance. In order to optimize energy consumption in buildings and subsequently improve their energy efficiency, buildings need to be monitored and various forms of copious data need to be collected to support DDDSA. The primary objective of this paper is to classify different data types that affect the energy consumption in buildings, and provide a comprehensive review of the existing data collection methods. The taxonomy developed, relevance of each data type along with the general characteristics of different data collection methods are discussed. Finally, challenges and limitations of gathering data with existing data collection methods in the context of building energy management are described along with proposed areas for future research.