Generalized Additive Modeling of Building Inertia Thermal Energy Storage for Integration Into Smart Grid Control

Abstract

The structural mass of a building provides inherent thermal storage capability. Through sector coupling, the building mass can provide additional flexibility to the electric power system, using, for instance, combined heat and power plants or power-to-heat. In this work, a mathematical model of building inertia thermal energy storage (BITES) for integration into optimized smart grid control is introduced. It is shown how necessary model parameters can be obtained using generalized additive modeling (GAM) based on measurable building data. For this purpose, it is demonstrated that the ceiling surface temperature can serve as a proxy for the current state of energy. This allows for real-world implementation using only temperature sensors as additionally required hardware. Compared with linear modeling, GAM enable improved modeling of the nonlinear characteristics and interactions of external factors influencing the storage operation. Two case studies demonstrate the potential of using building storage as part of a virtual power plant (VPP) for optimized smart grid control. In the first case study, BITES is compared with conventionally used hot water tanks, revealing economic benefits for both the VPP and building operator. The second case study investigates the potential for savings in CO 2 emission and grid connection capacity. It shows similar benefits when using BITES compared to using battery storage, without the need for hardware investment. Given the ubiquity of buildings and the recent advances in building control systems, BITES offers great potential as an additional source of flexibility to the low-carbon energy systems of the future.

A Future Direction of Machine Learning for Building Energy Management: Interpretable Models

“…As a result, the outdoor air temperature showed a negative relationship with the predicted heating load, while solar radiation had a negative exponential relationship with the predicted heating load. Voss et al [157] developed a mathematical model for the calculation of building inertia thermal energy storage (BITES) for smart grid control. They used a GAM to obtain BITES input parameters from building data, demonstrating that the ceiling surface temperature can be used as a proxy for the current state of energy use.…”

“…Their studies also investigated the energy costs related to selling and buying electricity, making the results interpretable in economic terms. In addition, GAMs were also used to perform sensitivity analysis on input features for thermal-comfort modelling [161] and thermal energy-storage modelling [157] and to identify operational patterns of gas-powered HVAC systems [162], distributed PV PP [163], and short-term energy prediction in buildings [156]. The main drawback of GAMs is their simplicity; they cannot be compared to more complex models but can only approximate the real behaviour of the system analysed.…”