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Data-Driven Methods To Optimize Building Energy Use

Buildings take a central place in human life. They are globally ubiquitous and are significant consumers of energy. In 2010, the building sector accounted for 19% of all global greenhouse gas (GHG) emissions and 32% of global final energy use, according to the IPCC.

Research shows that for conventional buildings, the operational phase accounts for up to 90% of the life cycle energy demand, with thermal end uses (space heating, space cooling, water heating) dominating the overall demand. According to the IPCC, space heating was the most significant end use in 2010, accounting for one-third of the global building stock’s final energy demand.

Applying the concept of Cyber-Physical Systems (CPS) is a promising strategy to reduce the lion’s share of building energy demand that is complementary to traditional means of building modernization, such as refurbishments.

“Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency” illustrates the typical stack of methods from data acquisition and data preparation to building automation system integration and optimization. Moreover, it touches on related areas, such as Multi-Agent Systems, Building Information Models (digital models building design and materials), weather normalization of energy demand, and possible interactions with the Smart Grid. However, at its core, the paper surveys recent research works that use data-driven methods to optimize building service systems’ operation.

For that, it provides an overview of 22 representative works and categorizes them according to the computational methods used, key performance indicators,  input data considered, building type studied, impact achieved, and the means of verification. The survey indicates that:

The overview of state-of-the-art advances in data-driven optimization of building system operation, as well as important related fields, motivates seven research questions. Each intends to guide future research work, as e.g., pursued in my Ph. D. thesis “EVOX-CPS: A Methodology for Data-Driven Optimization of Building Operation”.

These findings are described in the article entitled Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency, recently published in the journal Renewable and Sustainable Energy Reviews. This work was conducted by Mischa Schmidt from NEC Laboratories Europe and the Luleå University of Technology, and Christer Åhlund from the Luleå University of Technology.