13,930 research outputs found
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
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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
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