State-Space Modeling of Thermal Spaces in a Multi-Zone Building

A study on system identification and modeling of thermal spaces in a large institutional building is presented. The main topic of this paper is how the optimum model order associated which each thermal zone depends on factors such as the location of the zone within the building, its orientation and its exposure to outdoor space. Thermal models are essential in predictive control since they are required to predict the thermal load of a single building zone, a collection of various thermal spaces, or a whole building. The results of this study will serve as a guideline for choosing the appropriate order of linear models in similar buildings. The case study building is a model of a two storey school with a floor area of 24,000 m2 (258,000 ft2). The detailed thermal model of the building is created in EnergyPlus. This building model consists of 46 thermal zones covering a large variety of spaces: small offices, classrooms, long hallways and two gymnasia. The EnergyPlus is used to generate yearly input and output data available at 10-minute intervals; this data is used in a methodical system identification exercise, resulting in a set of multi-input single-output (MISO) state-space linear models. The challenge in modeling the thermal zones is to develop a relatively low-order model such that the thermal response of each zone is calculated by incorporating the effect of diverse inputs, such as outdoor factors (solar gains and outdoor temperature) as well as indoor factors, e.g., internal gains and heating and cooling energy delivered to the zone. Moreover, in a multi-zone building, accounting for the thermal effect of adjacent zones on one another is also an important factor to be taken into account. It has been found that this additional complexity requires careful selection of the inputs to the linear models, e.g., it might be helpful to include the heating/cooling delivered to adjacent zones

A Multi-level MPC Simulation Study in a School Building

This paper presents results obtained by applying a multi-level methodology for the implementation of a model-predictive control (MPC) strategy in a large institutional building. The case study building, is a model of a two storey school building, with a floor area of 24,000 m2 (258,000 ft2) with 46 thermal zones. The zones considered include a large diversity of spaces: small offices, classrooms, long hallways and two gymnasia. A detailed thermal model of the building was created in EnergyPlus. The EnergyPlus was used to generate input and output data employed for a systematic system identification exercise, which resulted in a set of multi-input single-output (MISO) linear models. Three control levels were considered: a thermal zone level (46 models), “wing†level (7 models) and a building level (one model). The models identified are state-space representations with order ranging between 4 and 12. This hierarchical, multi-level methodology enables the use of low-order models for each system under consideration: for example, a simple 9th order model at the building level can be used to predict its thermal load over a 48-h horizon, with a relatively coarse sampling time of 2 hours (24 samples). At the other extreme, a zone level model has a prediction horizon of 2 hours, and a much finer sampling time of 10 minutes (12 samples). For the MPC studies, a mechanical system considering thermal energy storage devices (ice bank + hot water tank) was considered in the calculations. An optimization routine was carried out to minimize the electricity cost, while maintaining comfortable conditions in the space: a time-of-use rate was employed in the definition of the objective function. The results presented in this paper illustrate how the multi-level concept discussed in this paper can be used to harmonize the performance of building control systems, from the supervisory BEMS to the local thermostat controllers

Control-oriented Modelling of Thermal Zones in a House: a Multi-level Approach

This paper presents a multi-level approach to the problem of modelling different thermal zones in a house for control applications. This problem has been treated before by modelling the whole house with a single, all-inclusive RC circuit which may have different levels of resolution. The core of the proposed methodology lies in the possibility of allowing the user to switch back and forth between models representing different control levels according to the modelling objectives. For the development of specific control algorithms for each zone, the house can be treated as a collection of interconnected zonal models, as opposed to a single, large model. This modelling approach has the advantage of maintaining a simple structure for each zone, while also taking into account the heat transfer between zones; at this control level, issues such as occupancy, thermal comfort or setpoint profiles can be examined in detail. On the other hand, if the user is interested in a quick estimate of global variables (e.g., overall thermal load over the next 24 h) then different zones or even the entire house may be combined into a single low-order model. In summary, this multi-level approach allows the user to “zoom in and out†so that models at each control level remain manageable, easy to calibrate and easy to physically interpret. This paper uses data from an existing unoccupied test house, representative of a typical family home in Québec, as a case study. Four zones are considered: basement, main floor, upper floor and the attached garage. For the most detailed analysis, these zones are modelled with four interconnected zone models. Alternative ways of combining zones are investigated. A global low-order house model is used to calculate the thermal load of the building. Results of thermal load calculations are compared and discussed

Preliminary Assessment of a Weather Forecast Tool for Building Operation

Although the potential of model predictive control (MPC) for the operation of buildings is widely recognized, as of today its adoption has been rather limited. This is partly due to the lack of user-friendly software tools for MPC, such as tools to facilitate the incorporation of forecast information in building automation systems. In view of this, CanmetENERGY, a research centre of Natural Resources Canada, has developed CanMETEO, a software tool free of charge aimed at obtaining weather forecast data and make it available in a useful and practical format for building operators. CanMETEO, which was released officially in August 2017, uses raw data produced by the Meterological Service of Environment Canada. This data, with high spatial resolution (e.g., 2 km x 2 km grids, and even denser for urban areas) enables the possibility of obtaining forecasts for very specific locations in the Canadian territory. Hundreds of weather variables (such as temperature, humidity, wind speed, cloud cover, among many others) are available for each point, which can be selected by the user via a graphical interface. The data is converted from GRIB files (a standard binary format used by meteorologists) into comma-separated value (CSV) files, which can be easily accessed. New forecasts become available every 6 hours, with a prediction horizon of 48 hours at hourly time steps; the retrieval of new weather forecasts can be setup in order to be performed automatically. These continuously updated CSV files may then be easily incorporated into building operation algorithms or simple optimization routines. Once the basic variables are obtained, post-processing calculations are applied in order to estimate solar irradiance on any given plane required by the user, for example, building façades and building-integrated photovoltaic panels. This feature also makes it possible to estimate the effect of solar gains on the thermal response of a building, and to estimate the output of photovoltaic panels. A preliminary evaluation of the tool, based on on-site measurements, is presented in this paper. It is expected that CanMETEO (currently used by Canadian research centre and universities) will provide one further step to the widespread adoption of predictive control as a viable, popular solution in building operation

Model-Based Control Strategies to Enhance Energy Flexibility in Electrically Heated School Buildings

This paper presents a general methodology to model and activate the energy flexibility of electrically heated school buildings. The proposed methodology is based on the use of archetypes of resistance–capacitance thermal networks for representative thermal zones calibrated with measured data. Using these models, predictive control strategies are investigated with the aim of reducing peak demand in response to grid requirements and incentives. A key aim is to evaluate the potential of shifting electricity use in different archetype zones from on-peak hours to off-peak grid periods. Key performance indicators are applied to quantify the energy flexibility at the zone level and the school building level. The proposed methodology has been implemented in an electrically heated school building located in Québec, Canada. This school has several features (geothermal heat pumps, hydronic radiant floors, and energy storage) that make it ideal for the purpose of this study. The study shows that with proper control strategies through a rule-based approach with near-optimal setpoint profiles, the building’s average power demand can be reduced by 40% to 65% during on-peak hours compared to a typical profile