The energy management in residential buildings according to occupant’s requirement and comfort is of vital importance. There are many proposals in the literature addressing the issue of user’s comfort and energy consumption (management) with keeping different parameters in consideration. In this paper, we have utilized artificial bee colony (ABC) optimization algorithm for maximizing user comfort and minimizing energy consumption simultaneously. We propose a complete user friendly and energy efficient model with different components. The user set parameters and the environmental parameters are inputs of the ABC, and the optimized parameters are the output of the ABC. The error differences between the environmental parameters and the ABC optimized parameters are inputs of fuzzy controllers, which give the required energy as the outputs. The purpose of the optimization algorithm is to maximize the comfort index and minimize the error difference between the user set parameters and the environmental parameters, which ultimately decreases the power consumption. The experimental results show that the proposed model is efficient in achieving high comfort index along with minimized energy consumption.
For every residential building, it is the most important issue to effectively manage the energy as well as achieve higher occupant’s comfort. The reason behind the fact is that the energy consumption increases rapidly with the passage of time and becomes more and more expensive and the user cannot compromise on his/her comfort. Therefore, the energy consumption minimization and user comfort maximization need to be balanced to achieve both goals. Therefore, a trade-off is required between the user comfort and the energy utilization [
Many energy management systems have been proposed in the literature for energy savings and consumption. In previous works, many approaches have been introduced which are based on conventional control systems [
In this paper, we propose an optimization methodology for maximizing user comfort and minimizing power consumption using multiobjective artificial bee colony optimization algorithm. Our proposed system is used for energy saving and achieving user comfort simultaneously. The aim is to integrate the fitness function of artificial bee colony with user comfort index and energy utilization. Artificial bee colony algorithm targets the user comfort and energy consumption for maximizing the first and minimizing the latter. User set parameters including temperature, illumination, and air quality are used as basic parameters in user comfort. These parameters are selected and then optimized using artificial bee colony according to the user comfort index. After ABC does the optimization, the error difference is found between the optimal parameters and the actual environmental parameters. This error difference is entered to fuzzy controllers as input and the output of the fuzzy controllers is the minimum power required for achieving the user comfort. The coordinator agent takes the output of the fuzzy controller (required power) as its inputs. Based on the available power from the power sources and required power from the fuzzy controllers, the coordinator agent adjusts the actual consumed power and gives this power to the actuators to change their status accordingly. The block diagram of energy efficient building is shown in Figure
Energy management building model.
In the proposed architecture, the temperature, illumination, and air quality from environment as well as from the user (user set temperature, illumination, and air quality) are entered to ABC optimizer. The ABC optimizer optimizes the environmental parameters according to user preferences to maximize the comfort index. The input to the fuzzy controllers (temperature fuzzy controller, illumination fuzzy controller, and air quality fuzzy controller) is the error difference between the environmental parameters and the optimized parameters. The output of the fuzzy controllers is the required power for controlling the status of the actuators (cooling/heating, lighting, and ventilation). The coordinator takes the required power as input and checks the availability of power from the power sources and provides the power to all the actuators according to status provided by the fuzzy controllers. The inputs of the fuzzy controllers are not only the ABC optimized values but also the environmental temperature, illumination, and air quality values. The output values generated by the fuzzy controllers depend upon the error differences between the environmental temperature, illumination, and air quality and the ABC optimized values for these three parameters. The main aim of the ABC optimization is to minimize these error differences. Without applying the ABC optimization process, the error differences are high, which ultimately generate higher output values causing higher energy consumption. After applying optimization, the error differences decrease causing optimized energy consumption. The proposed architecture is shown in Figure
Proposed architecture.
Artificial bee colony (ABC) is a nature inspired optimization algorithm which is based on the foraging behavior of bees. For the last few decades, many algorithms have been developed which have nature inspired behaviors. Some of them are evolutionary algorithms [ ABC is simple, robust, and flexible as mentioned by [ As compared to other optimization techniques, ABC has few control parameters [ ABC can be easily used in hybridization with other optimization techniques [ ABC has the capability to cope with objective function having stochastic nature [
The major steps of artificial bee colony optimization for optimization problem are described in the following section [ Step 1: Initialization of ABC parameters and problem specific parameters Step 2: Initialization of food source Repeat Steps 3 to 6
Step 3: Employed bees are sent to the food source Step 4: Onlooker bees are sent for selection of food source Step 5: Scout bees are sent for the search of new food Step 6: Best food source is memorized While (Objective is achieved or termination criterion is met)
(1) Number of parameters (
(2) Upper bound (
(3) Lower bound (
(4) Range (
(5) Colony size (SN): it represents the total number of solutions (food sources) in the population. This number is equal to total employed bees or onlooker bees. The algorithm has been tested for different number of colony sizes to find the best colony size to get best performance results.
(6) Foods: food represents the total population of the food source. The total population has been varied for getting the best optimization results.
(7) Maximum cycles (MC): They represent the maximum number of generations in algorithm run. The algorithm has been tested for different number of cycles.
(8) Limit (
(9) Objective function: this is the function we need to optimize. The algorithm has been developed to maximize the value of comfort index formulated in (
(10) Objective value: objective value represents value of objective function associated with each food source.
In order to initialize food source, we need
Food source is a matrix of size SN
In this stage (
for (
for ( End for Calculation of If end if
end for
The employed bees and onlooker bees have equal number of food sources. Initially, the selection probability of each food source generated by the employed bees is calculated by the onlooker bee. It then selects the best food source using Roulette selection method. The complete process in the onlooker bee phase takes place in Pseudocode
In this pseudocode,
for ( while ( end while for ( end for Calculation of If ( end if end for
The scout bees use (
For (
If ( end if
end for
In this phase, the source food and their position are memorized which gives maximum objective value.
Comfort index is computed using (
The concept of fuzzy has been introduced by Zadeh, a professor at California University at Berkley [
Structure of fuzzy controllers.
In the proposed architecture, the input to the temperature fuzzy controller is the error difference between the optimized temperature values from the optimizer and the environmental temperature. The output of the temperature fuzzy controller is the required power for cooling/heating system. The status of the cooling/heating actuators is changed according to the error differences between the actual environmental parameters and the artificial bee colony optimized parameters in which the output of the temperature fuzzy controller is the required power for the actuator status. The rules for temperature fuzzy controller are as follows and these are represented in Figures If ( If ( If ( If ( If ( If ( If (
In these rules,
The input to the illumination fuzzy controller is the error difference between the optimized illumination from the ABC optimizer and the environmental illumination. The output of the illumination fuzzy controller is the required power for lighting system. The status of the lighting actuators is changed according to the error differences between the actual environmental parameters and the artificial bee colony optimized parameters in which the output of the illumination fuzzy controller is the required power for the actuator status. The rules for illumination fuzzy controller are as follows and these are represented in Figures If ( If ( If ( If ( If ( If (
In these rules,
The input of the air quality fuzzy controller is the error difference between the environmental air quality and the optimized air quality from the ABC optimizer. The output of the air quality is the required power for ventilation system. The status of the ventilation actuators is changed according to the error differences between the actual environmental parameters and the artificial bee colony optimized parameters in which the output of the ventilation fuzzy controller is the required power for the actuator status. The fuzzy rules for air quality fuzzy controller are as follows and are shown in Figures If ( If ( If ( If ( If (
In these rules,
The coordinator takes the total power required for controlling the cooling/heating, lighting, and ventilation and provides the power available from the power sources. The total required power is computed by the following formula:
These are the devices inside buildings that actually use the energy. Examples of these actuators are AC (for cooling), heater (for heating), refrigerator (for cooling), and freezer (for cooling). The status of the actuators is changed according to the error difference between the environmental parameters and the ABC optimized parameters.
All the experiments were carried out on Intel(R) core(TM) i5-3570 CPU @ 3.40 GHz with MATLAB R2010a installed on it. Figure
User set, environmental and optimized temperature values.
User set, environmental and optimized illumination values.
User set, environmental and optimized temperature values.
Figure
User comfort index values with and without ABC optimization.
The second aim of the ABC algorithm is to minimize power consumption. This is achieved by minimizing the error differences between the user set parameters and the environmental parameters. Figures
Power consumption for temperature using ABC and without using ABC.
Power consumption for illumination using ABC and without using ABC.
Power consumption for air quality using ABC and without using ABC.
The total power consumption using ABC and without using ABC.
Input membership function for temperature.
Output membership function for temperature (required power for temperature).
Example of fuzzy rule applied to temperature.
Input membership function for illumination.
Output membership function for illumination (required power for lighting).
Example of fuzzy rule applied to illumination.
Input membership function for air quality.
Output membership function for air quality (required power for ventilation).
Example of fuzzy rule applied to air quality.
The error difference between the user set temperature and the ABC optimized temperature is input to the temperature fuzzy controller and the output of the temperature fuzzy controller is the minimum required power for temperature. The cooling/heating actuator status is changed according to this error difference. The error difference between the user set illumination and the ABC optimized illumination is input to the illumination fuzzy controller and the output of the illumination fuzzy controller is the minimum required power for illumination. The lighting actuator status is changed according to this error difference. The error difference between the user set air quality and the ABC optimized air quality is input to the air quality fuzzy controller and the output of the air quality fuzzy controller is the minimum required power for ventilation. The air quality actuator status is changed according to this error difference.
The authors in [
Power consumption comparison of ABC with GA and PSO.
Algorithm | Temperature power consumption | Illumination power consumption | Air quality power consumption | Total power consumption |
---|---|---|---|---|
GA | 439.19 | 1475.16 | 651.78 | 2566.14 |
PSO | 521.73 | 1531.01 | 694.54 | 2747.29 |
ABC [proposed approach] | 1023.74 | 941.38 | 547.86 | 2512.98 |
It is evident from the facts and figures given by the authors in [
In this paper, the issue of maximizing user comfort and minimizing power consumption in residential buildings using artificial bee colony optimization algorithm and fuzzy controller has been addressed. The whole architecture of the proposed system consists of different components including environmental parameters (temperature, illumination, and air quality), ABC optimizer, comfort index, fuzzy controller, coordinator, and different types of actuators. The inputs to the ABC optimizer are environmental parameters (temperature, illumination, and air quality) and user set parameters (temperature, illumination, and air quality). The outputs of the ABC optimizer are the optimized temperature, illumination, and air quality parameters. The inputs to the fuzzy controllers are the environmental parameters and the ABC optimized parameters and the outputs of the fuzzy controllers are the minimum power required to set the environment according to user preferences. The coordinator calculates the total power required sent by the fuzzy controller and checks the availability of required power. The statuses of the actuators are changed according to this power sent by the fuzzy controllers. The user comfort index has been increased and the power consumption has been decreased.
The authors declare that they have no competing interests.
This work was partly supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (no. 10043907, Development of High Performance IoT Device and Open Platform with Intelligent Software). And this research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2015-H8501-15-1017) supervised by the IITP (Institute for Information & Communications Technology Promotion).