# HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response

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## Abstract

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## 1. Introduction

- An original demand response optimization scheme is developed to include cost of energy and predicted mean vote (PMV) as the two criteria merged into one objective function. Along with HVAC hourly set points used as the variables of GA optimization, the developed approach constitutes a powerful assessment and decision tool which can be used to identify and ultimately apply dominant HVAC set point patterns based on actual weather conditions and preferences with regard to indoor conditions.
- The optimization algorithm coupled with the validated dynamic thermal model of the building enables the assessment of energy cost, energy savings, and thermal comfort for a wide range of temperature set point patterns and RTP schemes.
- The developed approach is designed to assess RTP schemes based on real DA market information to take advantage of price fluctuations which reflect current market operations in the optimization process.

## 2. Methodology and Infrastructure

#### 2.1. Methodology

_{HVAC}, kW), indoor air temperature (T

_{air}, °C), indoor radiant temperature (T

_{rad}, °C), and relative humidity (RH, %) for the period of interest (in this case, 2018). Fourthly, day-ahead pricing information was used to create the DARTP model required for the optimization. Day-ahead energy prices (€/MWh) for the region of central–northern Italy were used as the main component for the formulation of the energy pricing scheme used in the optimization. Additional costs related to transmission/distribution, as well as other costs and taxes, were included to define the final energy pricing profile. Fifthly, a genetic algorithm was constructed to optimize the objective function composed by (a) the daily sum of hourly cost of energy, and (b) the daily average of hourly PMV values for the working hours of the building and specifically from 9:00 a.m. to 6:00 p.m. In the GA optimization scheme, HVAC temperature set points were used as the discrete decision variables subject to upper and lower boundaries which differed between the heating and cooling seasons. Lastly, simulation of the validated building thermal model was executed in an iterative process using the set points selected by the GA until convergence criteria were met. Simulation output values of HVAC power, indoor air temperature, radiative temperature, and relative humidity were used to evaluate energy cost and the PMV at each iteration.

#### 2.2. Infrastructure

^{2}K and double-glazed windows with U-values between 1.793 and 3.194 W/m

^{2}K. It is equipped with an advanced energy management system controlling HVAC, artificial lights, energy from PV, and sensible thermal storage facilities. The building is equipped with a 236.5-kWp rooftop PV installation, a 400-m

^{3}thermal storage water tank, ground water heat pumps (heating coefficient of performance (COP) of 4.8, cooling energy efficiency ratio (EER) of 6.2–7), smart artificial light controls based on illuminance/presence sensors, and automatic rotating external window louvers. Operations are monitored and managed by MyLeaf, a state-of-the-art proprietary energy management platform customized for testing and integrating new concepts and technologies.

#### 2.2.1. GA optimisation model

- Metabolic (M) rate in W/m
^{2}; - Effective mechanical power (W) in W/m
^{2}; - Clothing insulation (Icl) in (m
^{2}K/W); - Air temperature in (°C);
- Mean radiant temperature (°C);
- Relative air velocity (m/s);
- Relative humidity (RH, %).

#### 2.2.2. Cost of energy model

## 3. Results and Discussion

^{2}and a height of 8 m, surrounded by various other spaces including offices, meeting rooms, and other facilities on two floors. Following a number of trials, the population size of the GA was set to 50, the crossover fraction was set to 0.8, and the maximum number of iterations was set to 4,600 in order to examine a wide range of different solutions. Based on the set of the results, solutions were obtained to identify the set point patterns associated with optimum levels of energy and cost savings, as well as compliance with well-established standards of thermal comfort and temperature set point drift. The approach was designed to evaluate energy cost on a 24-h time frame. Representative results for four winter days, two days for autumn, one for summer, and one for spring are presented to account for different seasonal climatic conditions, heating and cooling modes, and DA pricing profiles.

## 4. Conclusions and Future Steps

## Author Contributions

## Funding

## Conflicts of Interest

## Nomenclature

${T}_{s}{}_{i=1}^{24}$ | hourly temperature set points of the HVAC system the next day |

${w}_{c}$ | weighting coefficient for the daily operational cost of energy for the HVAC |

${w}_{pmv}$ | weighting coefficient for the daily thermal comfort |

${C}_{i}$ | day-ahead price per hour for hours 1–24 |

${P}_{i}$ | hourly average power consumption of the HVAC in kW (equivalent to kWh) |

${C}_{T}$ | total energy bill (€) |

IVA | value added tax (€) |

${C}_{E}$ | total energy charges (€) |

${C}_{T}$ | total tax charges (€) |

${C}_{S}$ | energy procurement cost (€) |

${C}_{N}$ | network services cost (€) |

${C}_{S,F}$ | energy procurement fixed cost component (€/kWh) |

${C}_{EDD}$ | daily excise duty on electricity and taxes (€) |

${C}_{v,u}$ | various costs normalized per kWh (€/Wh) |

$D{A}_{h}$ | day-ahead market prices (€/kWh) |

${C}_{F}$ | fixed cost component (€) |

${C}_{Pmax}$ | maximum power cost component (€/kW) |

${C}_{AT}$ | active energy cost component (€/kWh) |

${C}_{A-UC}$ | fixed cost for up to 4 GWh per month (€/kWh) |

$D{A}_{N,h}$ | DA price flexible factor per hour $h$ (€/kWh) |

${C}_{EDH}$ | excise duty per kWh (€/kWh) |

${C}_{FAA}$ | parameter to account for F, AT, and A-UC components (€/kWh) |

${C}_{Pmax,F}$ | maximum power fixed cost component (€/kW) |

Abbreviations | |

ADR | automated demand response |

AMI | advanced metering infrastructure |

COP | coefficient of performance |

CPP | critical peak pricing |

DA | day-ahead |

DER | distributed energy resources |

DR | demand response |

DSM | demand side management |

EER | energy efficiency ratio |

GA | genetic algorithm |

HVAC | heating, ventilation, and air conditioning |

MIP | Mixed Integer Programming |

MILP | mixed-integer linear problem |

MINLP | mixed-integer non-linear problem |

PMV | predicted mean vote |

PPD | percentage of people dissatisfied |

PV | photovoltaic |

RES | renewable energy sources |

RH | relative humidity |

RTP | real-time pricing |

R&D | Research and Development |

SDG | sustainable development goal |

ToU | time of use |

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**Figure 1.**Genetic algorithm (GA)-based heating, ventilation, and air conditioning (HVAC) temperature set point optimization scheme.

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**MDPI and ACS Style**

Kampelis, N.; Sifakis, N.; Kolokotsa, D.; Gobakis, K.; Kalaitzakis, K.; Isidori, D.; Cristalli, C. HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response. *Energies* **2019**, *12*, 2177.
https://doi.org/10.3390/en12112177

**AMA Style**

Kampelis N, Sifakis N, Kolokotsa D, Gobakis K, Kalaitzakis K, Isidori D, Cristalli C. HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response. *Energies*. 2019; 12(11):2177.
https://doi.org/10.3390/en12112177

**Chicago/Turabian Style**

Kampelis, Nikolaos, Nikolaos Sifakis, Dionysia Kolokotsa, Konstantinos Gobakis, Konstantinos Kalaitzakis, Daniela Isidori, and Cristina Cristalli. 2019. "HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response" *Energies* 12, no. 11: 2177.
https://doi.org/10.3390/en12112177