Abstract
Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.
Highlights
Building Management Systems (BMSs) provide monitoring and control capability for multiple sub-systems including Heating, Ventilation and Air Conditioning (HVAC)
The building is located on a top of a hill, so the surrounding ground was included in the model, but any effect of shading by neighbourhood constructions is neglectable
Once exported to EnergyPlus, the model was enriched with zone and activity modelling and the simulation time-step was set to 10-min intervals
Summary
Building Management Systems (BMSs) provide monitoring and control capability for multiple sub-systems including Heating, Ventilation and Air Conditioning (HVAC). Even in the case of good controller configurations, it is very hard to encapsulate efficient control strategies within a static set of simple rules, especially in large buildings with complex energy or renewable systems installed; here, all generation, distribution and room-level systems need to operate in a coordinated manner and be able to adapt to stochastic generation patterns that strongly depend on weather conditions [2] This manual configuration and control tuning process has been replaced by a methodology developed following two axes [2,3]:. In MPC application in buildings, typically, we assume that a model of the controlled process (i.e., the real building in our case) as well as (weather, occupancy, etc.) forecasts for a predefined time window are available This way, a constrained optimisation problem can be formulated and solved for a predefined period in the future, generating a control signal (or control strategy) which is optimised for the system on the basis of a defined cost function and a set of (visual, thermal, air-quality, etc.) comfort constraints. The remainder of this paper is structured as follows: Section 2 provides an overview of the MPC methodology in buildings; Section 3 describes the proposed methodology; Section 4 presents the experimental setup and the main results; and Section 5 presents the final conclusions and concrete steps towards future work
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