1

Intelligent HVAC’s management system at the IST-Taguspark campus

Sara Silva, Instituto Superior Técnico, Universidade de Lisboa

Abstract

Even with a 17% decline in energy consumption throughout all sectors since 2008 taken into account,

there was still a 6% increase in energy consumption in the service sector when compared to all other sectors.

The Barómetro da Eficiência Energética na Administração Pública points out that 40% of the energy consumed

in buildings is linked to heating, ventilation and air conditioning. Consequently, the use of forecasting and HVAC

control systems is increasingly important in this context.

This work intends to present an empirical forecast model, based on data history of the consumption of

the facilities with an external variable and the operating characteristics of the facilities themselves. In addition

to the model itself, a validation model is also presented in order to evaluate its adequacy on estimating the

thermal needs of the case study.

The first stage of this project was an analysis of the air conditioning system, the building management

policies, the case study’s air conditioning system and the also of the whole building. This analysis led to the

presentation of an hourly consumption model of the building, consisting of several profiles associated with

parameters such as time of the year, calendar of activities in the building, day of the week and the cycles

occurring in the air conditioning central, as well as an external variable, the maximum temperature reached

during the day, having produced a support tool for calculating profiles in Matlab.

This model presented some uncertainty in sensitive stages of the day in the cooling season, particularly

at the beginning and at the end of daytime resulting in average daily errors between 11.38% and 17.71%

depending on the yearly stage and calendar. In the heating season the results for the deviations were less

pronounced, getting up to a maximum of 7.88% of average daily error on vacation time.

Key-words: Electricity consumption, empirical model, air conditioning

1. Introduction

In Portugal, even if we take into account the steep decline in energy consumption across all sectors,

totalling around 17% since 2008, there has been an increase of 6% in consumption in the service sector when

compared to other sectors, highlighting the importance of energy consumption management in this sector, which

in general represents the energy consumption in buildings.

On the other hand, the Barómetro da Eficiência Energética na Administração Pública sugests that 40%

of the energy consumed in buildings is destined to HVAC [1]. From a system's management point of view, this

represents a substantial margin of energy savings potential. Because of this, the development and application

of decision tools that incorporate key concepts like thermic changes, meteorology and air quality have been

widely studied [2][3][4][5][6][7][8].

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Despite this, very few get to a situation of applying the control systems that they developed and when

they do, the chosen spaces represent low levels of utilization complexity. This way, the development and

application of a control model a HVAC system aimed at optimizing electricity consumption and reducing the

corresponding monetary value and maintenance of thermic comfort standards and air quality associated to a

complex, large building, should contribute to an easier and more automatized management by the management

members of service buildings. In this context, the forecast method of consumptions is integrated in this process,

also widely studied and being the basis of this dissertation.

Furthermore, the main objective of this work will be the construction of an empirical forecast model,

based on history data analysis of the facilities consumption and their variation with an external variable and with

the functioning characteristics of building, besides the validation of that model, in order to evaluate its adequacy

to estimate the terminal needs of the building.

2. Case Study Description

The case study is the Taguspark campus of IST, where the cooling needs of the building are satisfied

by the cooling central and the production of "cold" is assured by an ice storage system and two chillers. This

“partnership” is managed by a centralized management system that controls most operations.

Picture 1 - Cycles of the Central

The cooling central operates on the basis of cycles charging and discharging the ice storage. There are

four possible cycles, presented on Picture 1: the nocturnal charge cycle, the daily average consumption cycle,

the daily intense consumption cycle, and the emergency cycle. This last one happens in the eventuality of a

malfunction of emergency in the central. As soon as the charging period ends and begins the discharging period,

the daily average consumption cycle has started. When the reserves of the ice bank get to 30%, the chillers

turn on, beginning the daily intense consumption cycle. If the reserves get below 15%, the emergency cycle is

triggered and the chillers resume production of refrigerated water. The management cycle integrates the

Nocturnal Cycle Daily intense consumption cycle

Daily average consumption cycle Emergency Cycle

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schedule of the cycles and is responsible for operating the valves and other necessary components to each

cycle.

Graph 1 - Average Power used in business days in 2014, by reference period

3. Analysis to the Consumption

By analysing data from the building's

energy consumption, there is at the outset a clear

division between heating and cooling consumption

profiles. As seen in the Graph 1, the months from

November to May show a more stable behaviour

than the rest, with a consumption of ice storage

charging schedule similar to the rest of the daytime

so it can be concluded that in these periods there’s

no charge of the ice storage system.

Seeing the Graph 2, we asses that also

weekday, Saturday and Sunday present a variation

with each other, and throughout the year. In

addition, the behaviour of the consumer throughout

the day varies depending on time, and if the cooling

time, the cycles of the Central, as shown in Graph 4

and Graph 4.

0

50

100

150

200

250

300

350

400

450

2/jan 2/fev 2/mar 2/abr 2/mai 2/jun 2/jul 2/ago 2/set 2/out 2/nov 2/dez

Po

wer

[kW

]

Average Power used in business days in 2014

Horário Noturno Horário de Carregamento do BG Horário Diurno

0

1000

2000

3000

4000

5000

6000

7000

Ave

rage

Dai

ly C

on

sum

pti

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[kW

h]

Average Daily Consumption in 2014

Consumo médio em dias úteis Consumo médio aos sábados

Consumo médio aos domingos

Graph 2 - Average Daily Consumption in 2014

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3.1. Analysis to the correlation with the maximum temperature

The analysis to the correlation between

consumption and maximum exterior

temperature presented a satisfactory degree of

relation between the two variables. It was also

possible to get to a few conclusions regarding

the use of the daily intense consumption cycle in

several periods of the year, when comparing this

consumption with the maximum exterior

temperature. During the daily hours in days

where the discharge cycle used was the daily

average consumption cycle, the consumption

shows a tendency of the system to use this cycle

below 23ºC of maximum temperature during the

day. This is also shown when the same analysis

is done to the cycle of intense consumption,

getting to a maximum temperature during the

day of at least 21ºC. On the other hand,

analysing the days when the emergency cycle

was used it is possible to see a very reasonable

correlation with the maximum exterior

temperature, as shown in Graph 6.

In the Table 1 we have a summary of the analysis results of the correlation between consumption and

temperatures.

050

100150200250300350400450500

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:00

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:30

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:30

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:30

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:30

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:00

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:30

21

:00

22

:30

Po

wer

[kW

]

Power used in heating season

Graph 5 - Power used in heating season in a typical day

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:00

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Po

wer

[kW

]

Power used in cooling season

Ciclo Diário de Consumo Médio

Ciclo Diário de Consumo Médio + Intenso

Ciclo de Emergência

Graph 4 - Power used in cooling season in a typical day with several Central cycles

y = 45,832x + 3393,1R² = 0,4983

3500

3700

3900

4100

4300

4500

4700

4900

5100

5300

15 17 19 21 23 25 27 29 31 33 35 37

Co

nsu

mp

tio

n [

kWh

]

Maximum Temperature [°C]

Hourly Consumption in daily time and the maximum temperature

Graph 6 – Daily Consumption in daily time in days of emergency cycle and the maximum exterior temperature registered, with the

trend curve

Graph 3 - Power used in heating season in a typical day

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Period Classes Preparation for exams and exams Special stage of

exams

Cycles of the Central

Nocturnal Cycle + Daily

Average Consumption

Cycle

Emergency Cycle

Nocturnal Cycle + Daily

Average Consumption

Cycle

Nocturnal Cycle + Daily Average

Consumption Cycle+ Daily

Intense Consumption

Cycle

Nocturnal Cycle + Daily Average

Consumption Cycle + Daily

Intense Consumption

Cycle

Maximum Temperature [°C]

≤ 23 S/A ≤ 21 ˃ 21 ˃ 21

Relation Chf Vs

Tmax S/A

𝐶ℎ𝑓

= 45,832𝑇𝑚𝑎𝑥

+ 3393,1 S/A

𝐶ℎ𝑓

= 0,6743𝑇𝑚𝑎𝑥

+ 3314,6

𝐶ℎ𝑓

= 42,114𝑇𝑚𝑎𝑥

+ 2093,3

Determination Coefficient

S/A 0,4983 S/A 5 × 10−5 0,2607

Table 1 – Summary of the analysis on the consumption on daily time with the maximum temperature

4. Consumption Model

The proposed model of the consumption profile was conceptualized taking into account the

acclimatization system's parameters, the functioning parameters of acclimatized spaces and the analysis of the

correlation between maximum temperatures and consumptions. The profiles are based on the hourly

consumption averages between days in the same profile. Table 2 summarizes the parameters used in

determining consumption profiles.

Parameters

Season Cooling

Heating

Day Business

Weekends and Holidays

Period

Classes I

Preparation for exams and exams II

Special stage of exams III

Vacation period IV

Central cycles

Nocturnal Cycle + Daily Average Consumption Cycle

A

Nocturnal Cycle + Daily Average Consumption Cycle+ Daily Intense

Consumption Cycle

B

Emergency Cycle C

Central out of Operations D

Table 2 - Summary of the Parameters of the Model

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Besides those parameters, some correlations were developed using the days from each profile, between the

hourly consumption of maximum temperature reached during the day, in order to improve the profiles in cooling

seasons, and a support tool was created to aid the calculation of profiles in Matlab.

The Graph 7 and Graph 8 represent de hourly consumption of the profiles in business days for de heating

and cooling season, by parameter, where is not included the weekends and holidays.

Graph 7 - Consumption Profile in business days while in heating season, in classes, Exams and Vacation time

Graph 8 - Consumption Profile in business days while in cooling season, by cycle of the Central

80,0

100,0

120,0

140,0

160,0

180,0

200,0

220,0

240,0

Po

wer

[kW

]

Consumption Profile in business days while in heating season

Perfil em Aulas Perfil em Exames Perfil em Férias

0,0

100,0

200,0

300,0

400,0

500,0

Po

wer

[kW

]

Consumption Profile in business days while in cooling season

Perfil em Aulas com Ciclo A Perfil em Aulas com Ciclo C

Perfil em Exames com Ciclo A Perfil em Exames com Ciclo B

Perfiil em Época Especial com Ciclo B Pefil em Férias com Ciclo D

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4.1. Model Validation

a) Heating

Regarding the heating season, most

estimated profiles for business day’s present hourly

error when compared to real consumption below

10% during the day, in more detail, in 65% of days

in daytime and in 95% of days in night-time, as seen

in Graph 9. Because of this, daytime schedule is

particularly sensitive and in 25% of the days of the

profile, the hourly errors can get to 20% and in 5%

of the days, the hourly error stays between 20% and

40% [Graph 9]. On weekends and holidays of this

time of year there was an inferior hourly error with a

variation of less than 10% to the real hourly

consumption in about 95% in daytime schedule and

100% in nigh-time schedule [Graph 10].

The distribution of daily error during this season

on weekends, as well as the hourly error, doesn't get

above 10% in about 91% of days, while there is a small

percentage of 9% of days where the error to the real

consumption may get to 20%. With this, the maximum

average daily error was 7,88% in vacation period

[Graph 11].

b) Cooling

The validation of the model in the Cooling

season was widely difficult by the fact that one of the main

components of the Cooling central had a technical

malfunction. For this reason it was only possible to test the

prediction basing the comparison on profiles that included

the use of the emergency cycle. This way, four sensible

points in the profile in business days, were identified using

the emergency cycle:

3,57%2,89% 2,83%

6,73%5,95%

1,46%

7,88%

3,30%2,16%

0,00%

5,00%

10,00%

Dias Úteis Sábado Domingo

Dai

ly A

vera

ge E

rro

r

Heating Season

Período I Período II Período IV

0%

20%

40%

60%

80%

100%

Distribution of hourly error in business days

até 10% 10% a 20% 20% a 40%

40% a 60% 60% a 100%

0%

20%

40%

60%

80%

100%

Distribution of hourly error in weekends and hollidays

até 10% 10% a 20% 20% a 40%

40% a 60% 60% a 100%

Graph 9 – Distribution of hourly error in business days in the heating season

Graph 10 – Distribution of hourly error in weekends and holidays in the heating season

Graph 11 – Daily Average Error in the heating season

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The beginning of the daily schedule, from 7 to 10 a.m., when the chillers star to work and when

there’s a difference of 30 minutes that causes, in period I and III, an hourly error of over 100%

of the real hourly consumption in about 60% of days during Period III and in about 90% of days

during Period I, on the same schedule, that can be seen in Graph 13 and Graph 15.

The end of the daily schedule, from 7 to 9 p.m., when the chillers stop working, particularly in

Periods I and III, with a variation of consumption from the profile between 60% and 100% in

90% of days at 6 p.m. in period I and an hourly error over 40% in over 50% of days during

period III, that can be seen in Graph 13 and Graph 15.

The predicted hourly consumptions during daily schedule in business days are inferior to the

real consumption, especially during period III, when the hourly error is below 10% at 9 am in

around 10% of days and increases in frequency and magnitude until it gets to between 40%

and 60% of hourly error in 20% of days [Graph 15].

0%

20%

40%

60%

80%

100%

Period I

até 10% 10% a 20% 20% a 40%

40% a 60% 60% a 100% mais de 100%

0%

20%

40%

60%

80%

100%

Period II

até 10% 10% a 20% 20% a 40%

40% a 60% 60% a 100% mais de 100%

0%

20%

40%

60%

80%

100%

Period III

até 10% 10% a 20% 20% a 40%

40% a 60% 60% a 100% mais de 100%

0%

20%

40%

60%

80%

100%

Period IV

até 10% 10% a 20% 20% a 40%

40% a 60% 60% a 100% mais de 100%

Graph 13 – Distribution of hourly error on period I, in business days

Graph 12 - Distribution of hourly error on period II, in business days

Graph 14 - Distribution of hourly error on period IV, in business days

Graph 15 - Distribution of hourly error on period III, in business days

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In period IV, represented by the Graph 14,

there are some significant differences when

compared to the predicted profile. This is due to

different electricity consumption behaviour during

August, which is defined by a much inferior

consumption when compared to other months.

This didn't happen during the analysed year and

resulted in superior hourly and daily errors, and

there have been even some days when the

acclimatizing system was used and this was not

predicted by the created model.

On the other hand, despite the fact that non business days also register sensitive points similar to the

ones visible in business days, particularly hourly errors during the end of daily schedule from 20% to 40% in

30% of days and in the remaining days the hourly errors remained below 10% throughout the day.

The average daily error allows us to check that the sensitive points that are more evident in the

difference between real and predicted consumption are the ones that refer to the beginning and during the daily

schedule, mainly in business days in Period I with a daily average error of 12,46%, in Period III with 17,71%,

and on weekends during Period IV with 11,75% and 11,38% and 11,38%, respectively [Graph 16].

5. Conclusions

In this paper we developed a forecast model of electric consumptions of the Taguspark campus building

of IST, based on an analysis of its historical electricity consumption at different times and periods of 2014.

Because the case study concerns a building of which the electricity consumption has a high percentage

attributed to the air conditioning system, and the remaining being assigned to electricity consumption related to

building activities that have a variability between days of the same period much less relevant to the analysis

and much more complex, the forecast model focuses primarily on the management of the system.

The approach to modeling the consumption profiles was an empirical modeling, with an analysis of the

facilities’ consumption, resulting in the selection of various parameters that dependent on the conditions of use

of the facilities, the air conditioning and outdoor conditions. This approach, widely used in the literature, has

shown results that compete with large-scale physical models, supported by the advantage of computation time

compared to physical models.

For aggregation and better application of the forecasting model, a calculation program was developed

in Matlab, which features the profile provided for the selected day. Using the history data of the building’s

electrical consumption until the end of August 2015, it was possible to validate the obtained consumption

profiles. Unfortunately, in the case of the model in the Cooling season this validation was widely impaired by the fact

that one of the main components of the Cooling central had a technical malfunction, thus not being able to do a

complete validation of the model.

12,46%

0,10%

5,70%6,48%3,45% 2,76%

10,51% 11,75% 11,38%

17,71%

5,38%3,59%

0,00%

5,00%

10,00%

15,00%

20,00%

Dias Úteis Sábado Domingo

Dai

ly A

vera

ge E

rro

r

Cooling Season

Período I Período II Período IV Período III

Graph 16 - Daily Average Error in the heating season

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Nevertheless, it was observed with the validation that the forecasting model has a degree of uncertainty

in some of the presented profiles, particularly in periods when the air conditioning’s management system’s

behavior is different than the one assumed by the model and in various sensitive points of the day, for instance

the beginning and end of daylight hours and the increase in the consumption throughout the day, in the cooling

season. The heating season, when compared to the cooling, showed more modest deviations from the reported

consumption, which can be seen at monthly consumption, where months belonging to the heating season

register deviations up to 5,75% while cooling months can go up to 14,21%. On the other hand, the consumption

forecast in terms of daily and monthly values presents a good adjustment of the actual consumption model of

the building for both seasons. That being said, from the tool created in Matlab it is possible to manage several

parameters of the model, resulting in a simple application with control variables of easy access.

5.1. Future Improvements

The validation of the model was very limited because of the failure of the ice storage system, this being

an important component not only for the efficiency of the Central, but also for the parameters used in presented

model. A validation of the model after a reparation of the ice storage system and the use of sufficient data to

build a complete validation would probably resultant in an improvement of the model.

In addition, integrating real time response with features such as maximum temperature, registration of

changes in the Central, plus the implementation of a decision algorithm to allow optimization of energy

consumption over the state of the Central, would represent the first steps to create a more active management

system in Taguspark.

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