Adaptive Grey-Box Models for Model Predictive Building Control Using the Unscented Kalman Filter

Model predictive control (MPC) for buildings is a promising approach to reduce the energy consumption of buildings while at the same time the thermal user comfort can be improved. The core of this control strategy consists of building models that can describe the thermal behavior of particular zones accurately. Grey-box models are frequently used modeling approaches for control-oriented models, however, these models often have limitations regarding their general applicability. Furthermore, the modeling and identification of models used in MPC still require significant effort and is one of the main obstacles for the actual practical implementation of building predictive control. This paper addresses these issues and presents a framework for the online state and parameter estimation of grey-box models. The results show that (1) this online simultaneous state and parameter estimation highly increases the multi-steps-ahead (up to 48 h) prediction performance, (2) this approach enables the models to adapt to changing environmental conditions and (3) it is possible to use only one pre-defined initial model to describe the thermal behavior of several different zones

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Enabling Self-aware Smart Buildings by Augmented Reality

Conventional HVAC control systems are usually incognizant of the physical structures and materials of buildings. These systems merely follow pre-set HVAC control logic based on abstract building thermal response models, which are rough approximations to true physical models, ignoring dynamic spatial variations in built environments. To enable more accurate and responsive HVAC control, this paper introduces the notion of "self-aware" smart buildings, such that buildings are able to explicitly construct physical models of themselves (e.g., incorporating building structures and materials, and thermal flow dynamics). The question is how to enable self-aware buildings that automatically acquire dynamic knowledge of themselves. This paper presents a novel approach using "augmented reality". The extensive user-environment interactions in augmented reality not only can provide intuitive user interfaces for building systems, but also can capture the physical structures and possibly materials of buildings accurately to enable real-time building simulation and control. This paper presents a building system prototype incorporating augmented reality, and discusses its applications.Comment: This paper appears in ACM International Conference on Future Energy Systems (e-Energy), 201

Control of heat pumps with CO2 emission intensity forecasts

An optimized heat pump control for building heating was developed for minimizing CO2 emissions from related electrical power generation. The control is using weather and CO2 emission forecasts as input to a Model Predictive Control (MPC) - a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimized. In a simulation study the control was applied using weather and power grid conditions during a full year period in 2017-2018 for the power bidding zone DK2 (East, Denmark). Two scenarios were studied; one with a family house and one with an office building. The buildings were dimensioned on the basis of standards and building codes. The main results are measured as the CO2 emission savings relative to a classical thermostatic control. Note that this only measures the gain achieved using the MPC control, i.e. the energy flexibility, not the absolute savings. The results show that around 16% savings could have been achieved during the period in well insulated new buildings with floor heating. Further, a sensitivity analysis was carried out to evaluate the effect of various building properties, e.g. level of insulation and thermal capacity. Danish building codes from 1977 and forward was used as benchmarks for insulation levels. It was shown that both insulation and thermal mass influence the achievable flexibility savings, especially for floor heating. Buildings that comply with codes later than 1979 could provide flexibility emission savings of around 10%, while buildings that comply with earlier codes provided savings in the range of 0-5% depending on the heating system and thermal mass.Comment: 16 pages, 12 figures. Submitted to Energie