# Simulation-Based Evaluation and Optimization of Control Strategies in Buildings

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## Abstract

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## 1. Introduction

- Utilise predictions instead of historical data: Here, instead of gathering and analysing historical data from the building to improve the efficiency of specific controllers, forecasts (e.g., on weather, occupancy, equipment gains, etc.) are utilised to determine the (near-)future state (e.g., thermal/visual comfort conditions) of the building. This can enable for a continuous adaptation of the control logic to the (predicted) needs of the building and the microclimatic conditions of each site.
- Automated controller tuning: Here, an optimisation process is defined (utilising the available predictions) to design efficient controllers in an automated and laborious-free manner.

^{®}, which provide a remote (cloud-based) layer for hosting computationally demanding services, such as supervisory-level control design.

- installing additional sensors/meters required for accurate state estimation of the building to minimise the model/reality mismatch;
- configuration of the overall MPC solution (including model calibration) requires significant amount of time and the involvement of high-qualified engineers; and
- monitoring and solution debugging times of MPC solutions are significantly higher compared to the deployment of traditional knowledge-based controllers.

## 2. Model Predictive Control in Buildings

- t is the discrete time-step (with its length depending on the application), T is the prediction horizon.
- ${\mathbf{u}}^{\ast}:=\left[{u}_{1},{u}_{2},\dots ,{u}_{T}\right]$ is the optimal control solution.
- ${x}_{t}$ is a vector of values for each time-step t for the states of the system (e.g., wall and air temperatures [3]).
- The admissible control actions ${u}_{t}$ are a vector of values for each time-step t, with each value corresponding to a control action (e.g., temperature set-point, hot water flow, etc.) for all controllable systems of the building.
- ${w}_{t}$ is a vector of exogenous, uncontrollable factors affecting the system, like weather conditions or user actions. As discussed previously, we have some (reliable) predictions ${\widehat{w}}_{t}$ for these disturbances.
- $\mathbb{U}\subseteq {\mathbb{R}}^{m}$ and $\mathbb{X}\subseteq {\mathbb{R}}^{n}$ are closed sets defining the admissible controls and states respectively, while $\mathbb{W}\subseteq {\mathbb{R}}^{v}$ is the closed set of exogenous uncorrelated factors.
- $f:{\mathbb{R}}^{(n+v)}\times {\mathbb{R}}^{m}\to {\mathbb{R}}^{n}$ is a (possibly non-linear) function that describes the system dynamics.
- ${g}_{t}\in \mathbb{R}$ is the value of the cost (or objective) function to be minimised at time t and represents a performance measure, estimating the efficiency of a given control strategy. In the buildings domain, this index can either be economical (e.g., minimise operational cost) or environmental (e.g., maximise the net energy produced or minimise CO${}_{2}$ emissions).
- $C\left({x}_{t}\right)\le 0$ is a function usually describing thermal comfort constraints for the building occupants.

**Physics-based models**These models are designed based on first principles, utilising knowledge of the underlying physical process of the building and are usually analogous to electrical Resistance–Capacitance (RC) networks [3,6,17,18,19,20,21]. In an effort to reduce the model design- and calibration-associated costs for these models, a number of MPC approaches based on system identification methods [18,50,51,55,56,57,58] have been proposed. The problem here is that some initial (quasi-)random actions need to be performed to the real building (called persistent excitation signals in system identification theory) [51,56,59,60] to satisfy key theoretical assumptions on reliable statistical identification [55,61]. In real operation, this requirement is almost always infeasible without compromising the comfort or the health of the occupants (note that this is a problem closely related to the safe exploration/exploitation dilemma that has been extensively studied in reinforcement learning [52,53,62,63]). It is conceivable that a poorly identified model can lead to severe compromise of occupant comfort conditions; the model will provide inaccurate predictions that will lead to unsuitable controllers [64].

**Data-driven models**Here, a linear or non-linear regression model is fitted using available data from the real building and then utilised for the MPC application. Several types of regression functions have been reported in the literature, e.g. neural networks, support vector regression, autoregressive moving average, regression trees, etc. (see [5,65] for an overview). These models have the same data requirements as the system identification methods described before while they also suffer from poor extrapolation properties.

**BEP simulation models**When these models are utilised for control design, the main concern is the optimisation algorithm to be used, as they are characterised by high simulation times. A common approach is to assume that the simulation model is an exact model of the real building and construct a surrogate regression model off-line; this model is orders of magnitude faster compared to the original model so it could be used for MPC-like on-line control design [55,66]. From our experience with real-building control experiments [2,4,67], the assumption that the original simulation model (and thus also the surrogate model derived from it) can actually predict all states of the real building, under all different combinations of weather conditions and occupant actions, usually does not hold in practice. Instead, what is required is a continuous calibration process, for example as defined in the digital twin paradigm [68].

## 3. The Proposed Approach

- Design Phase:
- The Control Improvement module evaluates candidate control strategies utilising the simulation model under a set of pre-defined objectives (e.g., energy consumption, thermal comfort in each controller space, etc).
- When the Design Phase finishes, the best resulting strategy is communicated and deployed to the real building.

- Application Phase:
- The deployed controllers are used to generate new control actions in each control time-step for each controllable system of the building (possibly also utilising in-building and weather sensor measurements).

#### 3.1. Simulation-Based Evaluation Using Multi-Criteria Decision Analysis Methods

- The positive outranking flow ${\varphi}^{+}\left({S}_{i}\right)={\sum}_{{S}_{j}}\frac{pr({S}_{i},{S}_{j})}{N-1}$ expresses the degree to which a control strategy ${S}_{i}$ is preferred over all the other control strategies ${S}_{j}$.
- The negative outranking flow ${\varphi}^{-}\left({S}_{i}\right)={\sum}_{{S}_{j}}\frac{pr({S}_{j},{S}_{i})}{N-1}$ expresses the degree to which all the other control strategies ${S}_{j}$ are preferred over this specific one.

#### 3.2. Simulation-Based Optimisation Using Gaussian Process State Space Models

- The Squared Exponential (SE) covariance function, defined as:$${k}_{\mathrm{SE}}(x,{x}^{\prime})=exp\left(-\frac{\left|\right|x-{x}^{\prime}{\left|\right|}^{2}}{2{d}^{2}}\right),$$
- The Rational Quadratic (RQ), defined as:$${k}_{\mathrm{RQ}}(x,{x}^{\prime})={\left(1+\frac{\left|\right|x-{x}^{\prime}{\left|\right|}^{2}}{2a{d}^{2}}\right)}^{-a},$$
- The Matérn covariance functions, defined as follows:$${k}_{\mathrm{Matern}}(x,{x}^{\prime})=\frac{{2}^{1-\nu}}{\mathsf{\Gamma}\left(\nu \right)}{\left(\frac{\sqrt{2\nu}\left|\right|x-{x}^{\prime}\left|\right|}{d}\right)}^{\nu}{H}_{\nu}\left(\frac{\sqrt{2\nu}\left|\right|x-{x}^{\prime}\left|\right|}{d}\right),$$

- t, ${\mathbf{u}}^{\ast}$, ${u}_{t}$, $\mathbb{U}$, $\mathbb{X}$, $\mathbb{W}$, ${g}_{t}$ and $C(\xb7)$ have the same definitions as in Equation (1).
- ${\widehat{w}}_{t}$ is a vector of available (weather, occupancy, etc.) predictions.
- ${\widehat{x}}_{0}$ is provided by the simulator.
- $GP$ represents a set of GPs that provide an estimate of the states ${\widehat{x}}_{t+1}$ at time $t+1$, taking into account the states ${\widehat{x}}_{t}$, exogenous predictions ${\widehat{w}}_{t}$ and applied actions ${u}_{t}$ at time t.
- $\mathit{s}\left[{\sigma}_{1},{\sigma}_{2},\dots ,{\sigma}_{T}\right]$ is a vector containing the prediction variance in all time-steps t.
- $\u03f5$ is a design parameter. By requiring ${\parallel \mathit{s}\parallel}_{\infty}\le \u03f5$, we can implicitly allow for more or less exploration in the optimisation algorithm (i.e., favouring or not control actions that lead to “uncertain” states), simply by changing the value of $\u03f5$.

- An initial set of random simulations is performed in the detailed thermal simulation model. Note that, since we are operating in the simulation world, we can safely explore random or drastic actions with no effect to the real-building occupants.
- A set of GPs is trained using the simulated state, action and disturbance data.
- The best parameters discovered are simulated in the “expensive” thermal simulation model and the GPs are re-trained in a new dataset augmented with the most recent simulation data.
- The process re-iterates until convergence or a time-out occurs.

#### 3.3. Closed-Loop Control Extension

## 4. Experiments and Results

#### 4.1. The Example Building

#### 4.2. The Experimental Setup

- The building is controlled only during the occupancy period (08:00–17:00).
- The control actions are hourly setpoints, which are the same for the four control offices. Thus, the control time-step is one hour.
- The thermal comfort constraints are defined according to Fanger PMV index and are posed as illustrated in Equation (11).
- The (predicted) exogenous factors in our case are the weather conditions (provided by the weather file) and the occupancy patterns (which are assumed fixed and known in advance).
- Since we consider that the predictions for the disturbances are accurate, there is no need to re-design our control strategy at each control time-step (i.e., every hour), so for the work presented here we design the entire control strategy once for the entire day. Of course, in future application in the real building frequent control, (re-)design will be required to compensate for all unpredicted disturbances.
- The evolution of the system dynamics is provided by the EnergyPlus simulator; as described earlier we do not have access to the internal equations of the simulator but we can only observe the input-output relationship.
- The objective/cost function to be minimised is the total energy consumption measured at the VRF5 unit, which serves the four controllable offices.

#### 4.3. The Software Framework

#### 4.4. Results

#### 4.4.1. Approach Using Multi-Criteria Decision Analysis

- Weights: ${w}_{1}=10$ and ${w}_{2}=0$.
- Reasoning: This is a weighting combination that gives priority to the energy consumption.
- Result: The ranking of the strategies from PROMETHEE II algorithm is as expected, since the algorithm prioritises the solutions taking into account only the energy consumption criterion.

- Weights: ${w}_{1}=0$ and ${w}_{2}=10$.
- Reasoning: This is a weighting combination that gives priority to the thermal comfort conditions in the building.
- Result: The algorithm favours solutions that maintain comfortable interiors, but the highest ranking controller is the one with the minimum consumption among all the controllers that achieve comfort. This is one of the properties of the algorithm: even though we indicated that the energy consumption has no priority for us, the fact that this criterion needs to be minimised too leads to this sorting.We have to note here that, even though Strategy #9 leads to limited discomfort, it is not among the high-ranking strategies. This is due to the high priority given to the comfort criterion, which results into treating the constraint violations as hard constraints.

- Weights: ${w}_{1}=5$ and ${w}_{2}=5$.
- Reasoning: This is a weighting combination that gives equal priority to both criteria.
- Result: Here, the comfort violations are treated as soft constraints and the ranking of strategies is quite balanced, but the highest ranking strategy determined by the algorithm (Strategy #10) leads to significant discomfort in Office 87.

#### 4.4.2. Approach Using Gaussian Process State Space Models

- Input Features:
- ${\widehat{w}}_{t}^{1}$: Outdoor Air Temperature
- ${\widehat{w}}_{t}^{2}$: Global Solar Radiation in the Horizontal plane
- ${\widehat{x}}_{t}^{1-4}$: Indoor Air Temperature of the four controlled rooms
- ${\widehat{x}}_{t}^{5-8}$: Indoor Relative Humidity of the four controlled rooms
- ${\widehat{x}}_{t}^{9-12}$: Fanger PMV value of the four controlled rooms
- ${\widehat{x}}_{t}^{13}$: Energy consumption of VRF5 in the time-interval $(t-1,t)$
- ${u}_{t}$: the control setpoint

- Target values: all states at time-step $t+1$, i.e., ${\widehat{x}}_{t+1}^{1-13}$.

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 3.**Overview of simulation-based evaluation of building control strategies using multi-criteria decision analysis methods.

**Figure 5.**The Mafra City Hall Building: (

**a**) front view of the Mafra City Hall Building; (

**b**) simulation of Mafra City Hall Building using DesignBuilder Software [92]; (

**c**) the Mafra City Hall Building cut-off; and (

**d**) the Mafra City Hall Building floor-plan.

**Figure 6.**The experimental results for the controlled/occupied period within one simulation day: (

**a**) the performance of the simple rule-based controller with the setpoint 24.5${\phantom{\rule{0.222222em}{0ex}}}^{\circ}\mathrm{C}$; (

**b**) he performance of the best controller ranked by the MCDA method with the setpoint 25.5${\phantom{\rule{0.222222em}{0ex}}}^{\circ}\mathrm{C}$; and (

**c**) the performance of the optimised controller discovered by the GP_SS method with the setpoint varying per timestep, attaining values between 25.4${\phantom{\rule{0.222222em}{0ex}}}^{\circ}\mathrm{C}$ and 26.1${\phantom{\rule{0.222222em}{0ex}}}^{\circ}\mathrm{C}$.

**Table 1.**The evaluation of different control strategies in the simulation model. Here, the thermal comfort constrained violations are calculated using Equation (11) for the entire occupied period.

Strategy | Setpoint | Energy | Thermal Comfort | MCDA Results | |||||
---|---|---|---|---|---|---|---|---|---|

Index | Value | Consumption | Constraint Violation | ${\mathit{w}}_{1}=10$ | ${\mathit{w}}_{1}=0$ | ${\mathit{w}}_{1}=5$ | |||

(${\phantom{\rule{0.222222em}{0ex}}}^{\circ}\mathbf{C}$) | ($\mathbf{kWh}$) | R21 | R22 | R87 | R88 | ${\mathit{w}}_{2}=0$ | ${\mathit{w}}_{2}=10$ | ${\mathit{w}}_{2}=5$ | |

1 | 21.5 | 163.1 | 0.3 | 0.9 | 0.5 | 11.8 | 12 | 8 | 10 |

2 | 22.0 | 157.9 | 0.1 | 0.5 | 0.1 | 4.9 | 11 | 7 | 9 |

3 | 22.5 | 152.5 | 0.0 | 0.1 | 0.0 | 0.8 | 10 | 6 | 8 |

4 | 23.0 | 146.9 | 0.0 | 0.0 | 0.0 | 0.0 | 9 | 5 | 7 |

5 | 23.5 | 137.3 | 0.0 | 0.0 | 0.0 | 0.0 | 8 | 4 | 6 |

6 | 24.0 | 122.2 | 0.0 | 0.0 | 0.0 | 0.0 | 7 | 9 | 5 |

7 | 24.5 | 107.5 | 0.0 | 0.0 | 0.0 | 0.0 | 6 | 3 | 4 |

8 | 25.0 | 93.5 | 0.0 | 0.0 | 0.0 | 0.0 | 5 | 10 | 3 |

9 | 25.5 | 79.8 | 0.0 | 0.0 | 0.01 | 0.0 | 4 | 2 | 11 |

10 | 26.0 | 67.5 | 0.1 | 0.2 | 1.0 | 0.0 | 3 | 1 | 2 |

11 | 26.5 | 57.7 | 2.1 | 2.7 | 4.9 | 0.0 | 2 | 11 | 1 |

12 | 27.0 | 48.9 | 6.6 | 8.1 | 11.3 | 0.1 | 1 | 12 | 12 |

Energy Consumption RB (kWh) | Energy Consumption MCDA (kWh) | Energy Consumption GP_SS GPSS (kWh) |
---|---|---|

107.5 | 79.8 | 72.1 |

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## Share and Cite

**MDPI and ACS Style**

Kontes, G.D.; Giannakis, G.I.; Sánchez, V.; De Agustin-Camacho, P.; Romero-Amorrortu, A.; Panagiotidou, N.; Rovas, D.V.; Steiger, S.; Mutschler, C.; Gruen, G. Simulation-Based Evaluation and Optimization of Control Strategies in Buildings. *Energies* **2018**, *11*, 3376.
https://doi.org/10.3390/en11123376

**AMA Style**

Kontes GD, Giannakis GI, Sánchez V, De Agustin-Camacho P, Romero-Amorrortu A, Panagiotidou N, Rovas DV, Steiger S, Mutschler C, Gruen G. Simulation-Based Evaluation and Optimization of Control Strategies in Buildings. *Energies*. 2018; 11(12):3376.
https://doi.org/10.3390/en11123376

**Chicago/Turabian Style**

Kontes, Georgios D., Georgios I. Giannakis, Víctor Sánchez, Pablo De Agustin-Camacho, Ander Romero-Amorrortu, Natalia Panagiotidou, Dimitrios V. Rovas, Simone Steiger, Christopher Mutschler, and Gunnar Gruen. 2018. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings" *Energies* 11, no. 12: 3376.
https://doi.org/10.3390/en11123376