# Ergonomic and Economic Office Light Level Control

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

## 1. Introduction

- Power-saving lights, i.e., lights based on recently emerging light-emitting diode (LED) technology. These LED-lights exhibit an energy decrease of over 85% and last about 20 times as long [4] as standard lights; and
- Smart light regulators, i.e., regulating light levels according to the function of the area and functionality of the area. An advanced light controller acts according to the operator requests or predefined functionality per office area [5].

## 2. Materials and Methods

#### 2.1. Study Case

#### 2.2. Modeling

#### 2.3. Configurations

#### 2.4. Predictive Control Basics

`2×2`process model:

#### 2.5. Distributed Control Scheme

- Step 1: Subsystem i receives an optimal local control action $\delta {U}_{i}$ at the iterative time as $iter$ = 0 according to the EPSAC algorithm, and the local control action $\delta {U}_{i}$ can be rewritten as $\delta {U}_{i}^{iter}$, where $\delta {U}_{i}$ indicates the vector of the optimizing future control actions with a length of ${N}_{ci}$;
- Step 2: The $\delta {U}_{j}^{iter}$ (j $\u03f5$${N}_{i}$) is communicated to the subsytem i, and the $\delta {U}_{i}^{iter+1}$ is calculated again with the $\delta {U}_{j}^{iter}$ from the other subsystems;
- Step 3: If the terminal conditions $\left|\right|$$\delta {U}_{i}^{iter+1}-\delta {U}_{i}^{iter}$$\left|\right|$≤${\epsilon}_{i}$∨$iter+1>\overline{iter}$ are reached, the ${U}_{i}^{iter+1}$ is adopted, where ${\epsilon}_{i}$ is the positive value and $\overline{iter}$ indicates the upper bound of the number of iterations. Otherwise, the iter is set as $iter$ = $iter$ + 1, and return to step 2;
- Step 4: Calculate the optimal control effort as ${U}_{t}$ = ${U}_{base}$ + $\delta {U}^{iter}$, and the control effort is applied to the system;
- Step 5: Set the discrete time sample $t=t+1$ and return to step 1.

#### 2.6. Multi-Objective Optimization

## 3. Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- IEA—International Energy Agency. 2021. Available online: https://www.iea.org/statistics/co2emissions/ (accessed on 11 December 2021).
- Residovic, C. The new NABERS indoor environment tool, the next frontier for Australian buildings. Prodecia Eng.
**2017**, 180, 303–310. [Google Scholar] [CrossRef] - Heng, Y.; Fang, C.; Yuan, J.; Zhu, L. Design and application of a smart lighting system based on distributed wireless sensor networks. Appl. Sci.
**2020**, 10, 8545. [Google Scholar] [CrossRef] - U.S. Department of Energy. Energy Efficiency of LEDs. March 2013. Available online: https://www1.eere.energy.gov/buildings/publications/pdfs/ssl/led_energy_efficiency.pdf (accessed on 11 December 2021).
- Juchem, J.; Lefebvre, S.; Mac, T.T.; Ionescu, C.M. An analysis of dynamic lighting control in landscape offices. IFAC-Pap. Online
**2018**, 51, 232–237. [Google Scholar] [CrossRef] - Samad, T. A survey on industry impact and challenges thereof. IEEE Control Syst. Mag.
**2017**, 37, 17–18. [Google Scholar] [CrossRef] - Maxim, A.; Copot, D.; Copot, C.; Ionescu, C.M. The 5W’s for control as part of industry 4.0: Why, what, where, who and when—A PID and MPC control perspective. Inventions
**2019**, 4, 10. [Google Scholar] [CrossRef] [Green Version] - Cajo, R.; Zhao, S.; Cuvelier, F.; Lefebvre, S.; Leirens, B.; Juchem, J.; Ionescu, C.M. Effect of social distancing for office landscape on the ergonomic illumination. In Proceedings of the 3rd IFAC Workshop pn Cyber-Physical and Human Systems (CPHS), IFAC PAPERSONLINE, Beijing, China, 3–5 December 2020; Volume 53, pp. 762–767. [Google Scholar] [CrossRef]
- Juchem, J.; Muresan, C.; De Keyser, R.; Ionescu, C.M. Robust fractional-order auto-tuning for highly coupled MIMO systems. Heliyon
**2019**, 5, e02154. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Dounis, A.; Caraiscos, C. Advanced control systems engineering for energy and comfort management in a building environment: A review. Renew. Sust. Energy Rev.
**2009**, 13, 1246–1261. [Google Scholar] [CrossRef] - Dounis, A.; Tiropanis, P.; Argiriou, A.; Diamantis, A. Intelligent control system for reconciliation of the energy savings with comfort in buildings using soft computing techniques. Energy Build.
**2011**, 43, 66–74. [Google Scholar] [CrossRef] - Koroglu, M.; Passino, K. Illumination balancing algorithm for smart lights. IEEE Trans. Control Syst. Technol.
**2014**, 22, 557–567. [Google Scholar] [CrossRef] [Green Version] - Carli, R.; Dotoli, M. A dynamic programming approach for the decentralized control of energy retrofit in large-scale street lighting systems. IEEE Trans. Autom. Sci. Eng.
**2020**, 17, 1140–1157. [Google Scholar] [CrossRef] - Beccali, M.; Bonomolo, M.; Lo Brano, V.; Ciulla, G.; Di Dio, V.; Massaro, F.; Favuzza, S. Energy saving and user satisfaction for a new advanced public lighting system. Energy Convers. Manag.
**2019**, 195, 943–957. [Google Scholar] [CrossRef] - Quijano, N.; Ocampo-Martinez, C.; Barreiro-Gomez, J.; Obando, G.; Pantoja, A.; Mojica-Nava, E. The role of population games and evolutionary dynamics in distributed control systems. IEEE Control Syst. Mag.
**2017**, 37, 70–97. [Google Scholar] [CrossRef] [Green Version] - Haber, R.; Keviczky, L. Nonlinear System Identification Input-Output Modeling Approach; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1999. [Google Scholar]
- De Keyser, R.; Ionescu, C.M. The disturbance model in model based predictive control. In Proceedings of the 2003 IEEE Conference on Control Applications, Istanbul, Turkey, 25–25 June 2003. [Google Scholar] [CrossRef]
- Ionescu, C.M.; Copot, D. Hands-on MPC tuning for industrial applications. Bull. Pol. Acad. Sci.
**2019**, 67, 925–945. [Google Scholar] [CrossRef] - Cajo, R.; Ghita, M.; Copot, D.; Birs, I.R.; Muresan, C.; Ionescu, C. Context aware control systems: An engineering applications perspective. IEEE Access
**2020**, 8, 215550–215569. [Google Scholar] [CrossRef] - Fernandez, E.; Ipanaque, W.; Cajo, R.; De Keyser, R. Classical and Advanced Control Methods Applied to an Anaerobic Digestion Reactor Model. In Proceedings of the 2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), Valparaiso, Chile, 13–27 November 2019; pp. 1–7. [Google Scholar] [CrossRef]
- Maxim, A.; Copot, D.; De Keyser, R.; Ionescu, C. An industrially relevant formulation of a distributed model predictive control algorithm based on minimal process information. J. Process Control
**2018**, 68, 240–253. [Google Scholar] [CrossRef] - Rojas, J.D.; Arrieta, O.; Vilanova, R. Industrial PID Controller Tuning with a Multiobjective Framework Using MATLAB; AIC Book Series; Springer: Berlin/Heidelberg, Germany, 2021. [Google Scholar]
- Rossiter, J.A. A First Course in Predictive Control, 2nd ed.; CRC Press: Boca Raton, FL, USA, 2018. [Google Scholar]
- Xu, Z.; He, N.; He, L.; Ma, K. Event-based MPC for nonlinear systems with additive disturbances: A quasi-differential type approach. ISA Trans.
**2021**. [Google Scholar] [CrossRef] - Ionescu, C.M.; Caruntu, C.F.; Cajo, R.; Ghita, M.; Crevecoeur, G.; Copot, C. Multi-objective predictive control optimization with varying term objectives: A wind farm case study. Processes
**2019**, 7, 778. [Google Scholar] [CrossRef] [Green Version] - Ionescu, C.M.; Cajo Diaz, A.R.; Zhao, S.; Ghita, M.; Ghita, M.; Copot, D. A low computational cost, prioritized, multi-objective optimization procedure for predictive control towards cyber physical systems. IEEE Access
**2020**, 8, 128152–128166. [Google Scholar] [CrossRef]

**Figure 5.**Staircase experiment on every light with accompanying sensor values for half-height walls structure.

**Figure 6.**Various configurations for the office landscape area’s distribution of controlled zones. (

**a**) 4/4, (

**b**) 3/3/2, and (

**c**) 2/2/2/2.

**Figure 9.**The number of iterations executed by the distributed MPC algorithm for each of the following configurations for the office landscape area: (

**a**) 4/4, (

**b**) 3/3/2, and (

**c**) 2/2/2/2.

**Figure 12.**The number of iterations executed by the multi-objective distributed MPC algorithm for each of the following configurations for the office landscape area: (

**a**) 4/4, (

**b**) 3/3/2, and (

**c**) 2/2/2/2.

**Figure 13.**Implementation of external light and its configuration, via the input of additional bulbs (as seen in Figure 2).

**Table 1.**CPU time, iterations, ergonomics, and energy savings with respect to the configuration 4/4 (reference).

Control | Area | CPU Time (s) | Iter | Ergo (0–5) | Energy (0–100%) |
---|---|---|---|---|---|

DiMPC | 3/3/2 | 32.75 | 8 | 4.54 | 16% |

DiMPC | 2/2/2/2 | 31.76 | 6 | 3.53 | 17% |

MoDiMPC | 3/3/2 | 0.0453 | 8 | 3.23 | 16% |

MoDiMPC | 2/2/2/2 | 0.0443 | 6 | 3.87 | 17% |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Ghita, M.; Cajo Diaz, R.A.; Birs, I.R.; Copot, D.; Ionescu, C.M.
Ergonomic and Economic Office Light Level Control. *Energies* **2022**, *15*, 734.
https://doi.org/10.3390/en15030734

**AMA Style**

Ghita M, Cajo Diaz RA, Birs IR, Copot D, Ionescu CM.
Ergonomic and Economic Office Light Level Control. *Energies*. 2022; 15(3):734.
https://doi.org/10.3390/en15030734

**Chicago/Turabian Style**

Ghita, Maria, Ricardo A. Cajo Diaz, Isabela R. Birs, Dana Copot, and Clara M. Ionescu.
2022. "Ergonomic and Economic Office Light Level Control" *Energies* 15, no. 3: 734.
https://doi.org/10.3390/en15030734