IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings
Abstract
:1. Introduction
2. Related Work
2.1. NatureBased Algorithms for DSM
2.2. Fuzzy Inference System for DSM
3. Problem Formulation
3.1. Fuzzy Logic Controller
3.2. The Proposed Model
 (i)
 A system that helps the user to set a wide range of the thermostat setpoints based on the temperature and relative humidity.
 (ii)
 An energy management controller that will help in better energy optimization.
 (iii)
 A controller that can handle smart grid initiatives like ToU and DR that results in the energy management and conservation.
 (iv)
 The proposed controller will help in initializing the thermostat setpoints that maintain the temperature under user comfort zone.
 (v)
 FIS controller is designed in such a way that can include other parameters without having the hassle to define a large number of rules.
 (vi)
 The proposed controller includes room temperature variation that helps in making better decisions for the energy conservation.
 (vii)
 Using an IoT based fuzzy controller will result in realtime monitoring and total controllability.
3.3. Model of Residential Heating System
 Heater:Heater component is modeled using the amount of heat gain supplied to the room. When the heater is ON, hot air is blown at a constant temperature ${T}_{Heater}$ and a flow rate ${M}_{Heater}$. Heat gain inside the room is calculated using the equation below. Output of the heat is dependent on the thermostat control signal:$$\frac{d{Q}_{gain}}{dt}=({T}_{Heater}{T}_{Room})\times {M}_{Heater}\times {c}_{air},$$
 Thermostat:Thermostat in the room calculates the difference in the initialized thermostat value and room temperature and turns the heater ON or OFF based on the difference. Thermostat computes the difference at each 5min interval. The working of the thermostat can be summarized as follows:
 (i)
 When the room temperature is below the desired setpoint, the heater state is ON and it supplies the heat gain and value of control signal is equal to 1.
 (ii)
 When the room temperature is above the initialized setpoint for that particular time, the heater is turned OFF by the thermostat and heat gain is equal to zero as the control signal is 0.
 Room:In order to calculate the temperature variation, the system considers both the heat gain from the heater and heat loss from room to the surroundings. Heat loss ($\frac{d{Q}_{loss}}{dt}$) was calculated using the equation mentioned below where ${T}_{outside}$ is outdoor temperature at real time and ${R}_{thermal}$ is equivalent thermal resistance of the house:$$\frac{d{Q}_{loss}}{dt}=\frac{{T}_{Room}{T}_{outside}}{{R}_{thermal}}.$$In order to compute the variation in indoor temperature ($\frac{d{T}_{room}}{dt}$), heat gain and heat loss calculated using the Equations (6)–(7) two formulas were added in the following equation, where ${M}_{air}$ is the mass of air inside room:$$\frac{d{T}_{room}}{dt}=\frac{1}{{M}_{air}\times {c}_{air}}(\frac{d{Q}_{gain}}{dt}\frac{d{Q}_{loss}}{dt}).$$
4. System Model Implementation
4.1. Outdoor Temperature
4.2. Indoor Temperature
4.3. Occupancy
4.4. Price Tariff
4.5. Relative Humidity
4.6. Initialized SetPoints
4.7. Automatic FIS Rule Base Generation
 (i)
 In worldwide adaptive thermostat [28], there were four variables with three membership functions and one variable with two membership functions. This resulted in a total of 162 rules to be defined in the rule base of Mamdani FIS and Sugeno FIS.
 (ii)
 Adding humidity as a parameter to the system results in five variables with three membership functions and one variable with two membership functions. A total of 486 rules are required to be defined for the both Mamdani FIS and Sugeno FIS.
 (i)
 The first step is the fuzzification process in which all the membership functions of the system parameters are initialized and defined.
 (ii)
 The second step is defining the rules in the rule base by giving weightage to membership functions of input parameters and then assigning the suitable output fuzzy value.
 (iii)
 The third step uses the Mamdani FIS and Sugeno FIS to evaluate the energy consumption.
 (iv)
 After rule evaluation, defuzzification is performed to get the crisp value for the energy consumption. In the end, calculation of remaining performance measures is performed.
Algorithm 1 Automatic Rule Generator 

 (i)
 Temperature: Indoor temperature (${T}_{in}$) and outdoor temperature (${T}_{out}$) for both hot and cold cities have membership functions of (1) Low (L); (2) Medium (M); and (3) High (H) according to their cold and hot weather.
 (ii)
 Electricity Pricing: Pricing tariff ($Rate$) is classified into the following membership functions: (1) OffPeak (OP); (2) MidPeak (MP); and (3) HighPeak (HP).
 (iii)
 Occupancy: The user can either be present in the residential building or not. This condition is demonstrated using the membership functions of (1) Absent (A) and Present (P).
 (iv)
 Thermostat setpoints and Humidity: Both initialized setpoints ($ISP$) and humidity ($Humidity$) input parameters are classified in the membership function of (1) Low (L); (2) Medium (M); and (3) High (H).
 (v)
 Energy consumption: Output of the proposed FIS is energy consumed ($EC$), which is classified into the following membership functions: (1) Very Low (VL); (2) Low (L); (3) Medium (M); (4) High (H); and (5) Very High (VH).
5. Simulation Results
5.1. Results of FIS with Feedback
 1
 Scenario I:In Scenario I, outdoor temperature represented the coldest day that is below 0 ${}^{\circ}$C. As the temperature is very low, it is likely to take more energy consumption to maintain the inside temperature according to the user desired thermostat setpoint. Figure 9 represents the room temperature variation with respect to the initialized setpoints, outdoor temperature, and the heater state.
 2
 Scenario II:Outside temperature in this scenario depicted a sunny day where the outdoor temperature reached the desired thermostat temperature, in this case there was no need to turn ON the heater and the simulation results shown in Figure 14 depict the same behavior of the heater state in response to outdoor and indoor temperature. During the afternoon, outdoor temperature rises and the heater is kept in an OFF state in order to conserve the energy, as compared to the previous technique, where the heater is still ON and is utilizing the energy because the previous approach does not consider the variation in the room temperature.
5.2. Results of FIS with Humidity
 1
 Energy consumption with proposed FLC in Hot Cities
 2
 Energy consumption with proposed FLC in Cold Cities
 3
 Total Cost incurred with proposed FLC in Hot Cities
 4
 Total Cost incurred with proposed FLC in Cold Cities
 5
 Results for PAR using proposed FIS
 6
 User Comfort maintenance using proposed FIS
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Variables and Abbreviations  Description  Variables and Abbreviations  Description 

FIS  Fuzzy Inference System  HEMS  Home Energy Management System 
HVAC  Heating, Ventilation and Air Conditioning  PAR  PeaktoAverage Ratio 
PCT  Programmable Communicating Thermostat  ToU  Time of Use 
$Tem{p}_{indoor}$  Indoor temperature fuzzy input parameter  $Tem{p}_{outdoor}$  Outdoor temperature fuzzy input parameter 
$Occ$  Occupancy fuzzy input parameter  ${P}_{rates}$  Electricity rate fuzzy input parameter 
$IS{P}_{s}$  Thermostat set point fuzzy input parameter  $Humidit{y}_{rel}$  Relative humidity fuzzy input parameter 
Reference  Technique  The objective  A limitation 

An efficient power scheduling scheme [30]  Hybrid of Knapsack Problem (KWDO)  Minimization of the appliance waiting time and electricity cost.  Thermal comfort is ignored. 
Airconditioning system for proactive power demand response [32]  DSB and DFR  Cost and energy saving.  Use of synthetic dynamic prices and the system only works for offices. 
The smart thermostat: using occupancy sensors [37]  Hidden Markov Model for occupancy based scheduling  Improved energy conservation.  Thermal comfort is sacrificed. Simulations are limited to only one type of HVAC. 
Occupancy behaviorbased model predictive control [39]  Occupancy based Model Predictive Control (MPC)  User comfort enhancement and energy consumption minimization.  High computational cost and increase the complexity of the system. 
Hybrid Bacterial Foraging and Genetic Algorithm Optimization Techniques [40]  Hybrid of BFA and GA  Reduction in cost and PAR.  Thermal comfort is neglected. Only one pricing scheme is considered. 
Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids [41]  Hybrid of OSR with TLBO, FA, GA  Reduction in appliance waiting time, cost, PAR, and energy consumption.  Limited number of appliances. HVAC is not considered. 
Dynamic demand response controller based on RTP [42]  Dynamic Demand Response Controller (DDRC)  Energy consumption minimization  Narrow range of the temperature band is considered. User preference is ignored. 
A fuzzy logic system for demandside load management [43]  Fuzzy logic rule based algorithm  Demand response participation. Minimization of the energy consumption.  User comfort is sacrificed. 
An autonomous system via fuzzy logic [44]  Autonomous thermostat with Fuzzy Logic System  Energy conservation  Regionspecific study. Only Mamdani FIS is considered. 
An adaptive fuzzy logic system [17]  Adaptive Fuzzy Logic Model (AFLM)  Adapt the thermostat setpoints according to user comfort. Energy consumption minimization.  The proposed technique only considered the cold regions. User comfort is heavily disturbed. 
Worldwide adaptive thermostat using fuzzy inference system [28]  Worldwide adaptive thermostat  Works for both cold and hot cities. Reduction in peak, cost and energy consumption.  User comfort is jeopardized. 
i  ${\mathit{v}}_{\mathit{i}}$ 
1  $Tem{p}_{indoor}$ 
2  $Tem{p}_{outdoor}$ 
3  $Occ$ 
4  ${P}_{rates}$ 
5  $IS{P}_{s}$ 
6  $Humidit{y}_{rel}$ 
#Rule  ${\mathit{T}}_{\mathit{in}}$  ${\mathit{T}}_{\mathit{out}}$  $\mathit{Rate}$  $\mathit{Occupant}$  $\mathit{ISP}$  $\mathit{Humidity}$  $\mathit{EC}$ 

1  L  L  HP  A  L  L  VL 
2  L  M  OP  P  L  L  M 
3  L  H  MP  P  M  H  M 
4  M  H  OP  A  H  H  H 
5  M  L  MP  P  M  M  M 
6  H  M  OP  A  L  M  M 
7  H  H  OP  P  H  H  VH 
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Ain, Q.u.; Iqbal, S.; Khan, S.A.; Malik, A.W.; Ahmad, I.; Javaid, N. IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings. Sensors 2018, 18, 2802. https://doi.org/10.3390/s18092802
Ain Qu, Iqbal S, Khan SA, Malik AW, Ahmad I, Javaid N. IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings. Sensors. 2018; 18(9):2802. https://doi.org/10.3390/s18092802
Chicago/Turabian StyleAin, Quratul, Sohail Iqbal, Safdar Abbas Khan, Asad Waqar Malik, Iftikhar Ahmad, and Nadeem Javaid. 2018. "IoT Operating System Based Fuzzy Inference System for Home Energy Management System in Smart Buildings" Sensors 18, no. 9: 2802. https://doi.org/10.3390/s18092802