Energy efficiency of small buildings with smart cooling system in the summer

Abstract

In this paper, a novel cooling control strategy as part of the smart energy system that can balance thermal comfort against building energy consumption by using the sensing and machine programming technology was investigated. For this goal, a general form of a building was coupled by the smart cooling system (SCS) and the consumption of energy with thermal comfort cooling of persons simulated by using the EnergyPlus software and compared with similar buildings without SCS. At the beginning of the research, using the data from a survey in a randomly selected group of hundreds and by analyzing and verifying the results of the specific relationship between the different groups of people in the statistical society, the body mass index (BMI) and their thermal comfort temperature were obtained, and the sample building was modeled using the EnergyPlus software. The result show that if an intelligent ventilation system that can calculate the thermal comfort temperature was used in accordance with the BMI of persons, it can save up to 35% of the cooling load of the building yearly.

Graphical abstract

Keywords

smart home / heating and cooling systems / saving energy / optimal consumption of energy

1 Introduction

With the development of technology and programming and the increase of consuming energy especially electricity in the summer, it is so important to decrease the amount of peak load of electricity by using technology, programming and heating, ventilation, and air conditioning (HVAC) science. Smart energy systems are driven by the clear needs of concerning energy conservation and balancing building energy use against occupant comfort requirements. Smart energy systems would be able to advance building energy efficiency by monitoring, manipulating, and leveraging contextual information across the building environments [1].

Smart cooling of the building is considered as one of the main applications which has attracted great interest during the last few years [2]. The smart cooling is envisioned as the evolution of the current energy, which faces important challenges, such as blackouts caused by peaks of energy demand that exceed the energy capacity [3]. A purposed approach to alleviate this problem is to incentivize the consumers to defer or reschedule their energy consumption to different time intervals with a lower expected power demand. These incentives are based on smart (or dynamic) pricing tariffs that consider a variable energy price [4]. For instance, in real-time pricing (RTP) tariffs, the price of the energy will be higher at certain periods, where the energy consumption is expected to be higher, for example, in the afternoon or in cold days. Other types of smart pricing tariffs are critical-peak pricing (CPP) or time-of-use pricing (ToUP) [5,6]. Energy scheduling algorithms are the state-of-the-art methods to manage the energy consumption of loads within a smart pricing framework [7,8]. These techniques assume a specific smart pricing tariff and various periods. For each of these time intervals, the scheduler determines the operational power of each appliance to minimize the energy consumption cost. It is worth mentioning that the appliances that can be controlled by the energy scheduler can be categorized into three classes: nonshiftable, which do not admit any change on their consumption profile; time-shiftable, which tolerate postponing their operation, but not their consumption profile; and power-shiftable, whose operational power can be changed.

Building environment analysis is key to unlocking the potential for designing and implementing smart energy systems for mitigating energy use and balancing thermal comfort. Pan et al. [9] developed an intelligent light control system based on wireless sensor network (WSN) in indoor environments. They showed that this system can determine the proper illuminations of devices to achieve the desired optimization goals depending on the illumination requirement according to the user activities and profiles. Recently, data sets collected from WSN for a long period of time have been used in an attempt to perform automatic classification and clustering of indoor climates using machine learning technologies. For example, Gouy-Pailler et al. [10] collected a temperature data set for 10 days from 25 sensor nodes installed in a house. They calculated distance and similarity measures for sensor selection using Euclidean distance, complexity invariance distance, dynamic time warping, and event-based distance. With the clustering result based on the distance measured across all 25 sensors, they finally showed the potential of selecting sensors for studying thermal processes in highly-instrumented buildings. Lu et al. introduced the smart thermostat that automatically sensed the occupancy and sleep patterns in a home, and used them to save energy by automatically turning off the home’s HVAC system [11]. They evaluated the smart thermostat in eight homes by comparing the energy usage of the proposed method against existing standard control, achieving a 28% energy saving on average.

Recently, many researchers have been trying to combine the power of social networking and energy control systems, encouraging people to participate in energy savings. Weiss et al. [12] developed PowerPedia, a system that provides behavior-influencing feedback that can help occupants reduce the energy consumption of home appliances. PowerPedia allows occupants not only to interactively explore their energy consumption at a household and device level but also to compare their consumption with that of others by uploading the energy consumption data to a social community platform, a Wikipedia for electrical appliances plus Facebook and Twitter. As suggested in the PowerPedia system, introducing a social network like Facebook or Twitter to energy consumption feedback systems could encourage building occupants to take effective actions for improving energy use efficiency in buildings. This style of feedback systems through social networks echoes the motivation in the present paper. Besides, researchers usually use the EnergyPlus software for calculating the temperature or amount of energy that is consumed for cooling and heating of a building. For example, Ebrahimpour and Maerefat [13], Singh and Das [14,15] used the EnergyPlus software for simulation of building energy by single and hybrid HVAC systems respectively.

The aim of the present paper is to reduce energy consumption while keeping a high degree of comfort through the smart cooling approach, focusing on simulating smart buildings cooling load and comfort cooling of persons with smart cooling systems and the influence of sensual cooling on the total load of energy for small and general homes. For this purpose, a system of energy efficiency indicators (EEIs) and a rule-based system in which similar prototypes of it are investigated by Obasi et al. [16] and Halhoul Merabet et al. [17] can be used to set up the amount of consuming energy in the building for cooling in different conditions in accordance with different metabolism of persons. For example, different kinds of body metabolism is the reason for different feelings of the cool or hot environment at the same temperature for different persons and the level of physical activities of residents can affect the amount of energy-consuming. A smart system can set up an average between the feelings of different persons who are in a home. So, the amount of the energy-consumed can be reduced fast and thermal comfort cooling in a home can be provided. In the present paper, by using the EnergyPlus software, an emotional cooling system is simulated for evaluating the amount of total energy in the summer for cooling.

2 Mathematical modeling

The modeling is based on calculating the cooling load for general kinds of homes with this assumption that persons who are in the home have the same metabolism. After calculating the cooling load, it characterized the influence of different metabolisms on the cooling load and total energy-consuming.

2.1 Building description

For this purpose, an 80 m² home which is one unit of every floor in an apartment that is the most common and formal home in the city of Tehran is considered for the goal of the simulation. The boundary condition of this modeling is shown in Fig. 1. The goal home received specified amount of heat from all directions which are shown in Fig. 1. Also there are windows on the front wall but there are no windows on the back wall. All of equipment such as the refrigerator, lamp, stove and etc. are considered such as internal heat source. It should be noted that it is assumed that the average ambient temperatures in the summer are 40°C in Tehran.

Figure 2 shows the selected cooling system which is so common in Tehran. This system is based on a fan coil cooling system and reused the exhausted air in circulation for reducing the amount of energy consumption. The selected cooling system does not have any desiccant dehumidification. Therefore, the mean amount of humidity is considered to change hourly by the specify plan which is based on the weather condition in Tehran.

2.2 Material properties

The thermal properties of all materials are given in Table 1 [13]. The values are in accordance with ISO 12524-6, M19 [13,18]. The necessary design values of internal heat gains are listed in Table 2. The hourly variations of the internal heat gains are determined by the simulation schedules [13] that are depicted in Fig. 3. It is assumed that the daily average rate of ventilation for all zones are about 1 air change per hour. The impact of these parameters (internal heat gains, infiltration, and ventilation) on the results can be neglected.

2.3 Windows

As illustrated in Fig. 1, the intended sample building has three windows with specified dimensions. Windows can have great impacts on the amount of heat exchanged (entering or exiting the building). Therefore, it is necessary to determine the type of windows and their associated thermal properties in the simulation. Table 3 shows the type of window which is commonly used in buildings in Tehran [13].

2.4 Weather data

For performing a true simulation of building energy, it is necessary to have the weather data. The EnergyPlus simulation software requires the hourly data such as dry bulb temperature, dew point temperature, solar radiation, humidity, wind speed, and direction, etc. Since weather conditions can vary significantly for each year, it is very important to derive the typical meteorological year (TMY) data [19]. In the present paper, the TMY of the weather data in Tehran is generated using two different software (Weather generator [20], Meteonorm [21]). These data have been compared with the one measured by the meteorology office of Tehran, and have been validated [22]. The simulations are performed from June, 21 to September, 21 in 2016. The relative monthly humidity and dry bulb temperature (minimum, daily average, and maximum) for Tehran, Iran are demonstrated in Fig. 4.

Figure 5 illustrates the direct, diffuse, and global average solar radiation, and mean monthly wind speed in Tehran, Iran.

2.5 Validation

The experimental results reported by Sabzi et al. [23] for the average hourly temperature in a typical building in Shiraz, Iran and its climatic conditions are compared with numerical results obtained from the simulation, as exhibited in Fig. 6, to ensure the accuracy of the simulation. The slight difference between the experimental and the numerical results verifies the validity of the simulations and the reliability of the results in the present paper.

2.6 Different kinds of metabolisms

To achieve a complete and comprehensive definition of thermal metabolism that has a direct connection with the thermal comfort of persons, it is necessary to consider the parameters that may affect the thermal comfort of individuals. This helps to properly manage the amount of energy used to create comfortable conditions. To achieve this goal, the present paper attempts to provide a measure of thermal comfort (thermal metabolism) based on parameters such as gender, height, weight, and type of cover used by people at home that are related to the specific culture of individuals.

Given the fact that thermal comfort is a sensory and mental measure and has a direct relationship with the temperature that a person may feel comfortable with, it is necessary to collect the information from several individuals in an interview. That is why the information about 100 people who have been randomly selected has been gathered through the survey and the way they are categorized is discussed.

The parameter used to communicate between the height and weight in the present paper is the body mass index (BMI) that is calculated using Eqs. (1) and (2) for men and women respectively.

(1)\mathrm{BMI}={\displaystyle \frac{\mathrm{weight}}{{\mathrm{height}}^2}},formen,

(2)\mathrm{BMI=}{\displaystyle \frac{\mathrm{weight}}{{\mathrm{height}}^2}}\mathrm{+1},forwomen\mathrm{.}

Based on the results of the survey, the participants in the survey are divided into four categories, which are presented in Table 4. As can be seen in Table 4, each of the four groups defined based on the BMI has specific percentages of the surveyed community. For example, the highest percentage of people belongs to group A and the lowest percentage of people in this community is related to group D (people with excess weight).

Besides, based on the results of the survey of the type of coverage of people in the indoor environment, three temperature ranges (TH, TM and TC) for its basic thermal is considered. The first temperature range (TH) is for those whose coverage is complete. These people are more likely to be present at higher temperatures. Therefore, the thermal comfort temperature for these people is considered to be above 27.5°C. In the second temperature setting (TM), the coatings have the proper mode, meaning that the thermal comfort of these people is at normal temperature. Therefore, the temperature range suitable for the thermal comfort of this group is considered to be between 25°C and 27.5°C. The third temperature range (TC) is reserved for people who feel cool. The coverage of these people in the home is usually low. Therefore, the temperature for heating is lower than 25°C. In the present paper, the temperature ranges suggested for TH, TM, and TC are similar to the broader ranges of indoor temperatures that are used by Zhang et al. [24] based on the ASHRAE database.

In Fig. 7, the results of the classification of thermal groups in the form of circular diagrams for each of the four BMI groups are presented.

As can be deduced from Fig. 7, there is a temperature range for each of the four groups defined by BMI (A, B, C, and D) based on the data from the survey conducted by the majority of people in that category. If they are exposed to the temperature, they will be provided with the highest level of mental satisfaction and thermal accommodations. Based on the analysis of the results of the survey, between the thermal comfort temperature and BMI of each group of people, a relation can be found which is shown in Fig. 7 that the thermal comfort temperature for people in groups A and B is TH, for people in group C is TM, and for people of group D is TC.

Another important parameter that can significantly affect the thermal comfort temperature of indoor buildings and the amount of energy consumed by HVAC systems is physical activities. The effect of physical activities and the classification of a different kind of them are listed in Table 5 [25].

In the present paper, the superscript symbols of ⁺ and ^{+ +} are used for the thermal comfort temperature of mean and heavy physical activities while light physical level is considered as default mood. Therefore, it does not have any symbol. The HVAC system should do more work to provide the thermal comfort condition for residents. For example, TC⁺ represents the thermal comfort temperature of a person who is in the thermal category of TC and has a mean level of physical activity in the building.

2.7 Assumptions

To simplify the simulation of the problem in this paper, it is assumed that the number of people in the building is three; the physical activities of residents are limited, for example, at least one of them is in light level, the uttermost number of the people with mean and heavy physical levels are 2 and 1 respectively; the metabolic, thermal nature and level of physical activity can mentally be captured by an intelligent system.

3 Results of software simulation

All possible conditions for the thermal comfort temperatures are considered and analyzed with respect to real characteristics of sample building. The thermal load of home for each of the scenarios are simulated by using EnergyPlus to determine how much energy can be reduced by using an intelligent cooling system. In Table 6, all possible conditions for the thermal comfort temperature of the persons in the sample building are visible, and for each of them, a name has been selected.

Note that the basis for calculating the cooling load for all the cases discussed in this paper is to achieve the average temperature of the thermal comfort of individuals in the sample building in each case, which is calculated as the weighted average of the thermal comfort of the persons in the building and the application is placed.

The relationship used to calculate the average temperature of thermal comfort is expressed in Eq. (3) and the relationship used to calculate the ratio of the amount of energy efficiency of cooling load is expressed in Eq. (4) as shown.

(3)T_{\mathrm{AVE}}={\displaystyle \frac{{\displaystyle \sum _I^N{T}_I}}{N}},

where T_AVE is the mean thermal comfort temperature and N is the number of people that are in the building and in the present paper, it is equal to 3.

(4)cop={\displaystyle \frac{e_{\mathrm{o}}-e_{\mathrm{n}}}{e_{\mathrm{o}}}}.

In Eq. (4), the term cop is the cooling load efficiency, e_o is the cooling load of the sample building with the assumption that the thermal comfort temperature is the lowest thermal comfort temperature of the people in the building, and e_n is the cooling load of the sample building with the assumption that the thermal comfort temperature is calculated by the smart air conditioner using Eq. (3).

In Fig. 8, the results of the simulation with the software are presented in the form of reduced cooling load consumption. The results show that for the first three groups, due to the similarity of the maximum and average temperature of the thermal comfort, the change in the consumption rate of cooling is equal to zero. But for the other groups, there has been a dramatic decrease in cooling consumption. For example, the maximum and minimum reductions of consumption for cooling the building have been observed in g10 and g5 modes, respectively, with decreases of 29.2% and 17.1%, respectively.

As can be seen in Fig. 8, the effects of the thermal groups on the thermal nature and BMI of the individual are a function of two parameters. The first parameter is the thermal comfort temperature inequality and the second is the weighted average thermal comfort temperature. For this reason, g1 to g3 do not show any difference in the use of conventional and intelligent ventilation systems due to the same thermal comfort temperature. However, where the comfort temperature of all three individuals are different for this reason, g10 has the highest energy savings compared to other modes when using the intelligent ventilation system. g4 to g9 have the same inequality and the same weighted average thermal comfort temperature. Therefore, the amount of energy saved by each of them is approximately 19%.

To observe the effects of physical activity of occupants in the building on the amount of energy saving, according to the rules stated in the Section 2.6, all possible modes of physical activity for the heat of g2, g8, and g10 are shown in Tables 7 to 9, respectively. Concerning the similarity of the changes in the energy efficiency of g1–g3 groups, g2 has been selected as the representative of this group. Besides, g8 is the selected representative of g4–g9 groups.

The results of the comparison of the effect of the physical activity of residents on energy storage are shown in Figs. 9 to Fig. 11. It is clear that by considering the physical activity of individuals in situations where the temperature of each person’s thermal comfort varies with others, the rate of increase in efficiency will be higher. The simultaneous effect of the mean increase on the thermal comfort temperature and physical activity can be seen in comparing g21 and g22. The presence of two thermal comfort temperatures which is equal to TC⁺ in the case of g23 is due to the decrease in the rate of increase in efficiency relative to g22.

The results of comparison of subgroups in terms of physical activity for g8 and g10 are visible in Figs. 10 and 11, respectively. The dominant mechanism for decomposing these two forms is also the difference in temperature comfort temperature and weighted average temperature comfort temperature.

It is obvious that as much as the difference between the average and the maximum thermal comfort of the people in the building is higher, the idea of using intelligent ventilation systems is more economical.

While cooling the building using this type of intelligent system, the maximum degree of satisfaction with the thermal comfort temperature can be achieved, since the setting of the ambient temperature is not dependent solely on the individual choice, and all the inhabitants in the building will choose the optimum temperature.

4 Conclusions

In this paper, a survey has been conducted among a randomly selected 100 population and after collecting information and analyzing them, the thermal comfort temperature for the various groups of people in the community based on BMI, determined by a high satisfaction rate.

After determining the thermal comfort temperature of the people in the population, a very common building in the city of Tehran is modeled. By applying a few simplistic assumptions, the cooling load of this building is calculated using EnergyPlus for three people in different states with respect to their physical activities and compared with each other.

Comparing the results of the cooling consumption of the building under different conditions for residents, it can be deduced that the intelligent ventilation system can recognize the thermal comfort temperature BMI, making it possible to reduce the amount of energy consumption by 30% each year.

In conclusion, the results of this paper indicate that the optimum management of manufacturing equipment can provide maximum thermal comfort and significant reduction in cooling and ventilation energy of building, simultaneously.

One of the significant limitations for development and extensive use of the intelligent ventilation system may be the high initial cost of the system. Therefore, saving the energy from longtime operations of the intelligent ventilation system and increasing the economic benefits should be more discussed and highlighted for construction builders.

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