Abstract
We investigate data-driven, simple-to-implement residential environmental models that can serve as the basis for energy saving algorithms in both retrofits and new designs of residential buildings. Despite the nonlinearity of the underlying dynamics, using Koopman operator theory framework in this study we show that a linear second order model embedding, that captures the physics that occur inside a single or multi zone space does well when compared with data simulated using EnergyPlus. This class of models has low complexity. We show that their parameters have physical significance for the large-scale dynamics of a building and are correlated to concepts such as the thermal mass. We investigate consequences of changing the thermal mass on the energy behavior of a building system and provide best practice design suggestions.
Highlights
The “House as a System” approach is gaining traction as a protocol to gain deep energy efficiency in residential buildings [1]
We show that despite the nonlinearity of the underlying dynamics, in this study we show that a linear second order model embedding, that captures the physics that occur inside a single or multi zone space when given a specific data set
We fit the model parameters to simulated temperature data from EnergyPlus and we show that the reduced order model is able to capture the nonlinear thermodynamics
Summary
The “House as a System” approach is gaining traction as a protocol to gain deep energy efficiency in residential buildings [1]. The cost of such retrofits has led to development of strategies for optimizing operations of existing systems, focusing first on fault detection and returning the building operation to a “healthy” state [2]. Beyond the fault detection methodologies, model-based approaches lead to optimization of existing systems and potential of deep energy savings for new commercial builds [3], and even. US Army facilities [4] These gains are not currently utilized in the context of residential buildings. There are about 136.5 million residential buildings in the United States [5], creating a large opportunity for energy savings via retrofits and new designs, to create more efficient homes. In order for retrofits and newly designed systems to work properly, smart technology must be introduced and implemented
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