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Towards Efficient Energy Management in a Smart Home Using Updated Population

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Energy management using demand side management (DSM) techniques plays a key role in smart grid (SG) domain. Smart meters and energy management controllers are the important components of the SG. A lot of research has been done on energy management system (EMS) for scheduling the appliances. The aim of current research is to organize the power of the residential units in an optimized way. Intelligent energy optimization techniques play a vital role in reduction of the electricity bill via scheduling home appliances. Through appliance’s scheduling, consumer gets feasible cost in response to the consumed electricity. The utility provides the facility for consumers to schedule their appliances for the reduction of electricity bill and peak demand reduction. The utility company is allowed to remotely shut down their appliances in emergency conditions through direct load control programs. A lot of research has been done on energy management system (EMS) for scheduling the appliances. In this work, an efficient EMS is proposed for controlling the load in residential units. Meta-heuristic algorithms have been used for the optimization of the user energy consumption schedules in an efficient way. Our proposed scheme is used to minimize the user waiting time. User waiting time is inversely proportional to the total cost and peak to average ratio (PAR). Simulation result shows the minimum user waiting time, however, the total cost is compromised due to the high demand of the load. In the end, our proposed scheme will be validated through simulations.
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Towards Efficient Energy Management
in a Smart Home Using
Updated Population
Hafiz Muhammad Faisal1, Nadeem Javaid1(B
), Zahoor Ali Khan2,
Fahad Mussadaq3, Muhammad Akhtar3, and Raza Abid Abbasi3
1Comsats University Islamabad, Islamabad 44000, Pakistan
nadeemjavaidqau@gmail.com
2Computer Information Science, Higher Colleges of Technology, Fujairah 4114, UAE
3NCBA&E, Multan, Pakistan
http://www.njavaid.com
Abstract. Energy management using demand side management (DSM)
techniques plays a key role in smart grid (SG) domain. Smart meters and
energy management controllers are the important components of the SG.
A lot of research has been done on energy management system (EMS)
for scheduling the appliances. The aim of current research is to organize
the power of the residential units in an optimized way. Intelligent energy
optimization techniques play a vital role in reduction of the electric-
ity bill via scheduling home appliances. Through appliance’s scheduling,
consumer gets feasible cost in response to the consumed electricity. The
utility provides the facility for consumers to schedule their appliances for
the reduction of electricity bill and peak demand reduction. The utility
company is allowed to remotely shut down their appliances in emer-
gency conditions through direct load control programs. A lot of research
has been done on energy management system (EMS) for scheduling the
appliances. In this work, an efficient EMS is proposed for controlling the
load in residential units. Meta-heuristic algorithms have been used for
the optimization of the user energy consumption schedules in an efficient
way. Our proposed scheme is used to minimize the user waiting time.
User waiting time is inversely proportional to the total cost and peak to
average ratio (PAR). Simulation result shows the minimum user waiting
time, however, the total cost is compromised due to the high demand
of the load. In the end, our proposed scheme will be validated through
simulations.
Keywords: Smart grid ·Home energy management system ·
Real time price
1 Introduction
Conventional energy production systems cannot fulfill the user’s need. It gives
rise to a number of different challenges that are faced by the electric power
c
Springer Nature Switzerland AG 2020
L. Barolli et al. (Eds.): AINA 2019, AISC 926, pp. 39–52, 2020.
https://doi.org/10.1007/978-3-030-15032-7_4
40 H. M. Faisal et al.
industry. Approximately, 10–30% energy is wasted in the methods used to sup-
ply the electric power from the source to end consumers. As we know the power
generation and power utilization is a one-way process so, the power generation
system is unable to control and manage the electricity consumption. Tradition-
ally, power generation systems and its demand side management (DSM) methods
are centrally distributed and only focused on industrial customers. Smart grid
(SG) solve maximum problems that are present in conventional energy produc-
tion systems. The bi-directional flow of power between the electricity generation
system and the end consumer allow many factors to be controlled to play a vital
role in energy wastage. The bi-directional communication is not only concerned
by the consumers for electricity price and maintenance schedules of the distri-
bution network, however, also motivate the providers to monitor and analyze
the real time power utilization data. Distributed energy system and information
technology are involved in the SG. Using SG, communication can be handled
and monitored with the proper mechanism. Recent developments of the SG allow
load management methodologies to be implemented more efficiently by permit-
ting them to utilize new technologies. DSM and demand response (DR) are
important components of SG. Between the power grid and consumers, DR play
a vital role. DR schemes are used to reduce the electricity consumption without
affecting the user’s comfort. Two types of DR programs are considered:
Incentive base DR
Time based DR
Using the incentive-based DR program, utility can turn ON and OFF the con-
sumers appliances with the term notice. Whereas Time based DR requires the
consumers participation for scheduling their according to the change in price sig-
nals. DR is very helpful in reduction of the consumers electricity bills based their
participation. The core objective of using multiple DR and DSM methodologies
is to alleviate the burden of the electricity bill and energy usage. The objec-
tive of sellers is to increase their profits whereas consumers want their personal
incentives. Smart meters (SM) are installed in the residential area. The rela-
tionship between SM and DSM consist of many different and connected parts
with many features and perspectives to consider. Without regard to exceptions,
the target of SM usage is to minimize energy consumption as well as peak for-
mation without losing the consumers comfort. SM provides the user’s complete
information of demand, supply and price signal. There are some methodologies
including mixed integer linear programming (MILP), linear programming, and
mixed integer non-linear programming (MINLP), etc., which can be applied for
minimization of the energy consumption expanses while managing the efficient
schedules of electricity. DSM itself is not a technique; it is more generally a col-
lection of strategies used to change the user’s energy patterns for obtaining their
suitable power distribution. The collection of strategies (i.e. DSM) encourage
the consumers to monitor and control multiple factors (e.g. power load, appli-
ance management) of power consumption which results in lower power wastage.
This can be achieved by applying various artificial intelligence algorithms. In
Towards Efficient Energy Management 41
DSM strategy consumers perform load management and shifting the load into
different time duration.
2 Related Work
Using MILP in [1] as a design technique, the main objective of this method
is to minimize the peak to average ratio (PAR) value and the total electricity
bill paid by consumers. The authors also focuses on balance load management
but the user comfort is not considered in MILP. There is a trade-off between
conventional systems and today’s renewable energy sources (RES). SG is a two-
way communication of utility and consumers and by saving this 10–30%, we cover
a lot of energy wastage problem. Cost minimization is a big challenge for today’s
researchers in SG. Using a genetic algorithm (GA) technique in the research
article [2], cost minimization is achieved at a low level with the integration of RES
and stored energy. When price and demand are higher than the stored energy
is helpful in specific time changes. Deployment and maintenance cost of storage
devices and RES has been ignored in this technique. Majority of researchers
have focused on residential areas only. Balancing the load in commercial and
residential areas is a big problem. However, using GA-DSM [3] algorithm in peak
hour, electricity consumption is reduced by 21% in an industry which is very
remarkable. The authors neglected the PAR value and user comfort feasibility.
The authors in [4] proposed scheme of MINLP is used to solve the cost min-
imization under the price tariff ToU. Although the cost minimization at the
peak hour is achieved, however, the author disregarded the PAR value which is
another important factor in SG. Dynamic programming [5] technique was used
for cost minimization and by scheduling the gadgets for various duration. This
will be done by the integration of RES and energy storage systems (ESS) with
SG. Residents have the capability to produce the electricity from RES. A con-
sumer can sell the additional electricity to the neighbours. The important factor
of installation and maintenance has been ignored in RES. The novel schedul-
ing model [6] of the combination of GA and binary partial swarm optimization
(BPSO) algorithms. The goal of this technique is electricity bill and PAR min-
imization. In DSM, the user can manage their home appliances by shifting the
load to another time so the load demand is a key factor in this regard. By shifting
the load and by using cuckoo search algorithm (CSA) [7] algorithm peak load
has been reduced by 22%. Balanced load curve generated by the CSA algorithm
shows the user preference for appliance usage, load shifting can then be done
by using this curve. For real-time schedule controller, new binary backtracking
search algorithm (BBSA) [8] was proposed. By using the load limit home appli-
ances are shifted from peak hour and electricity price reduced 21% per day in
comparison to PSO algorithm. Huang et al. [9] developed the two point esti-
mation method embedded with PSO method for reducing the computational
complexity in a home energy management systems (HEMS). This scheme is
intelligent enough in comparison to GPSO-LHA in the context of computational
burden. Author [9] did not consider the cost of electricity and PAR value which
42 H. M. Faisal et al.
are the important factor in HEMS. The author in [4] proposed using MINLP
technique cost minimization is achieved under the price tariff of ToU however,
PAR is not considered in this technique. Utilizing DSM, 30% power utiliza-
tion can be minimized without knowing the usage on the user side. The overall
objective of load management is to schedule a load during high demand to low
demand intervals. This can be done by the combination of the GA algorithm and
bacterial foraging (BF) in [10]. Hybrid technique [10] minimizes the electricity
bill and PAR using the load management shifting. Hybrid scheme uses the RTP
signal for reducing the electricity bills and PAR.
In HEMS, SG has a significant functionality to minimize the users’ cost using
DSM. Meta heuristic scheme is designed in [11] for the reduction of cost and PAR
value. Applying the combination of GA and CSA, we achieved the minimized
cost and PAR value as well, under the RTP price tariff, as compared to other
techniques with the desirable user waiting time. In DSM user performed pri-
orities that are set to schedule the appliances in HEMS. The authors proposed
[12] evolutionary accretive comfort algorithm (EASA) which is comprised of four
postulations. These postulations are defined according to the time and device
bases priorities. Based on the input values EASA generates optimal energy solu-
tion pattern which satisfies the user budget. The author defines three different
user budgets to find the absolute solutions. Ma, Yao, et al. [13] defines discom-
fort function for two different type of gadgets. First category is flexible starting
time and the other is flexible power devices. Authors in [13] considered a multi-
objective function for user comfort and cost parameters.
The proposed bat algorithm in [14] can be applied to obtain the optimum
result. By applying this algorithm energy consumption can be reduced which
is simply a non-linear optimization problem. The main goal of current work is
to decrease the power usage and increase consumer comfort standards in the
residential area. In Al Hasib [15], the author considered bidirectional energy
exchange between SG and small residential building. The main goal of this paper
is to maintain the balance between electricity cost and user comfort. Here the
appliances load was categorized into three categories. Based on a declining block
rates (DBR), the author proposed a comfort demand function. The authors in
[16] recommended a min max load scheduling (MMLS) algorithm used to reduce
the PAR while optimizing the operational comfort level (OCL) of user’s. It is
important to note the difficulty faced by user’s, under the control of HEMS when
reducing power consumption. For residential demand response, the author has
proposed an intelligent algorithm which analyze the effect of HEMS operations
on the use’r comfort [18].
The authors in [19], smart homes are integrated with SG to purchase/sell
electricity in peak load demand. The proposed scheme objective is to minimize
the cost and PAR along with the increase in earning profits. In this model two
optimization techniques, CSA and strawberry algorithm (SA) are used with RES:
wind turbine (WT), photovoltaic panels (PV) and ESS. The simulations results
show that the proposed scheme efficiently reduce cost and PAR with maximiz-
ing earning’s. CSA optimization technique outperforms than SA to minimizing
Towards Efficient Energy Management 43
cost and PAR during peak load demand. Many techniques and models in [2025]
were addressed using SG and micro SG with standalone and connected-grid with
HEMS are the emerging research areas in the last few years. In [26,27], many
authors have proposed scholastic programming models however, dynamic pro-
gramming schemes in [28,29] were proposed. These static and dynamic models
need precise tuning in their algorithm to manage the parameters and to control
them. The authors in [30] proposed DSM model, where RESs are connected.
The proposed model consist of three layers: the utility, the customer and the
DR aggregator. The role of the DR aggregator has been defined as a mediator
that communicate with both customers and the utility. The experimental out-
comes demonstrate that consumers can get profit from the proposed design: the
DR aggregator can make the profit by providing DR services; the utility can
reduce the generation cost; customers can save money on their monthly elec-
tricity bill. Evolutionary algorithms are used for load shifting in order to reduce
the cost of the customers [32]. All service sides have data sets, where schedul-
ing problem have been managed to solve the efficiency problem, the industry
faced more problem because of big power consumption appliances. Due to high
load user’s need to use the energy more intelligently in both residential and
commercial sector.
3 Problem Formulation
DSM techniques are proposed to handle the irregular consumption of electric-
ity which is the complex task to tackle. Consumers require more electricity at
certain time intervals, so, there is a possibility for peak formation and electric-
ity blackouts. In this situation, intelligent algorithms are required for EMS to
help user’s for scheduling the power from high demand intervals to low demand
intervals in an effective manner. Most of the techniques have been designed to
reduce the peak formation, electricity cost and user’s discomfort [1,33,35]. How-
ever, there is always a trade-off between PAR, electricity bill and user comfort
standards. RES integration is lacking in [1] for enhancing the comfort standards
of the residents. In [33], appliance priorities are not considered in an automatic
fashion. The study in [35] prioritizes the appliances manually; however, they need
automatic priority specifications for controlling the whole system efficiently. So,
there is need to design an EMS which can optimize the energy consumption of
the residential sector consumers efficiently. Meta-heuristic algorithms are used
for the optimization of the energy consumption schedules defined by user’s.
4SystemModel
We have developed a home energy management (HEM) method for controlling
the energy consumption load and price of the smart homes. Initially, this algo-
rithm starts with a single smart home and 15 appliances in it. These appliances
are categorized into two main categories: schedulable and non-schedulable appli-
ances. Smart meter decides the operation time of the appliances according to
44 H. M. Faisal et al.
their power rating and defined pricing tariff from the utilities. The power rating
varies for each appliance and scheduling of these appliances is done in such a
way to achieve the optimum solution from the designed objective function. In
this work, two pricing tariffs are used: RTP and CPP for checking their impact
on the customers electricity bills. DR and DSM used in SG provide more sta-
bility and reliability in grid operation. The aim of this work is to reduce the
PAR, energy consumption and cost, and to enhance the consumers preferences
according the consumers standards. Main architecture of this system is visual-
ized in Fig. 1. The electricity bill is conveyed to consumers through the smart
meter. HEM controller decides which appliances should be turned on using the
defined pricing signals during the peak and off-peak hours. The core objective
of this study is to reduce the power utilization, PAR and electricity cost while
maximizing the user comfort. However, there is always a trade-off which occurs
between electricity bill and consumers preferences. Total energy utilization is
formulated using Eq. 1. Equations 2and 3is used to calculate the PAR and cost
respectively.
Load =
24
t=1
(PRS(t)),S(t)=[1/0] (1)
PAR =(Max(Load)/Av g (Load)) (2)
Cost =
24
t=1
(PP PRS(t)),S(t)=[1/0] (3)
Fig. 1. System model
Towards Efficient Energy Management 45
5 Optimization Algorithms
Traditional optimization algorithms, which belongs to mathematical techniques,
are not working satisfactorily if a large number of devices exists. Computa-
tionally power is also slow and time consuming. Behind this reason, we apply
heuristic schemes grey wolf optimization (GWO) and Jaya algorithm to obtain
our objectives. We proposed a JGO algorithm, which is discussed in details in
the subsection below.
5.1 GWO
GWO is a novel meta-heuristic algorithm. It consists of four types of wolves:
alpha, beta, delta and omega. There are three main phases of hunting in GWO.
Alpha is used as the most fittest solution between beta, delta and omega. List
of main steps of GWO is given.
1. Encircling the prey
2. Hunting
3. Exploitation: It is also called attacking the grey
4. Exploration: It is also known as search for prey.
5.2 Jaya Algorithm
Suppose f(x) is the target function to be minimized or maximized. Assume that
there are “m” number of design variable and “n” number of candidate solutions,
at any iteration i, where k = 1, 2, 3, 4.....n. Let the best value of f(x) (i.e.
f(x)best) is obtained by the best candidate names as “best” and vice versa for
the worst candidate (i.e. f(x)worst). If the value for jth variable during the ith
iteration for kth candidate is Xj,k,i then this value is modified as per the following
equation given below:
´
Xj,k,i =Xj,k,i +r1,j,i (Xj, best, i..........) (4)
Where the value of j variable, for the “best” candidate is Xj, best, i and vice
versa for the worst candidate, where the updated value of Xj,k,i and r1,j,i and
r2,j,i which are the two random number for the jth variable is X’j,k,i in the range
of [0,1].
5.3 Updated Population
In this section, we described our proposed scheme. The population update is
performed in GWO, the updating is totally dependent on the placement of the
primary three accurate candidates. So in GWO, we tend to note initial 3 good
solutions, oblige the other wolves or search agents to change and update their
locations on the idea of the placement of the most effective search agent. Here,
Eq. 5is used to find the effective search agent location.
X(t+1)= X1+X2+X3
3(5)
46 H. M. Faisal et al.
Hence, we are able to say that Jaya population update strategy is good as
compared to the GWO as a result of here, our aim is to search out the more
effective result as possible and also the a lot of optimized result may be possible
by having a large random and various population. Where initialization of GWO
is better than Jaya initialization. So, we selected Jaya based population update
strategy and GWO based initialization strategy, so that, we proposed a new
proposed algorithm.
6 Pseudo Code of the Proposed Scheme
1. Generate initial search agents
Gi(i=1,2, ...., n) (6)
2. Initialize the vector’s
3. Calculate the fitness value
4. Iter = 1
5. repeat
6. for i = 1:
7. end for
8. Calculate the fitness value of all solutions
9. Update the value
10. Update the vectors
11. Iter = Iter+1
12. until Iter maximum number of iterations
13. output best solution.
7 Simulation and Reasoning
In this section, we demonstrate simulation results and evaluate the performance
of the proposed algorithm. The load, cost and user’s waiting time for each appli-
ance are represented in terms of hours, cents and kWh. By applying RTP signal
in a smart home, we achieve maximum user comfort time, however, at some level
cost and PAR values are maximized. There is always a trade-off between PAR,
cost and user comfort. In RTP price signal tariff, electricity price varies during
different time slots of a single day. Figure 2shows the complete details of price
per hour in 24 h. In afternoon, the price rate is two times higher. Per hour cost is
increased due to unbalancing of the price at peak time. This is shown in Fig. 3.
Figure 4shows the hourly based energy consumption pattern in both scheduled
and unscheduled scenarios.
Figure 5represents the PAR value of GWO, Jaya and proposed algorithm. It
demonstrates that proposed technique result is better than the GWO algorithm,
however, Jaya algorithm is better than our proposed algorithm. PAR value of
proposed algorithm is 50% less than the GWO so as compared to GWO we
achieves PAR goal in a smart home, however; Jaya technique is suitable for the
Towards Efficient Energy Management 47
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
8
10
12
14
16
18
20
22
24
26
28
Time (hour)
Price (cent/kWh)
RTP
Fig. 2. Total cost
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
50
100
150
200
250
Time (hour)
Per hour cost (cent)
Unscheduled
GWO
JGO
Jaya
Fig. 3. Per hour cost
reduction of PAR in RTP signal. Figure 6shows the total cost of GWO, Jaya and
proposed algorithm. It is shown that the overall cost of the algorithm is higher
as compared to GWO and Jaya. This effects the overall cost per day in the RTP
signal. Reduction of electricity cost is the core objective of DSM in SG. With
the comparison of Jaya and GWO, cost of our proposed technique is higher due
to higher user demand. Figure 6shows the total cost of three different schemes
in terms of cents. Complete load details of unscheduled load, Jaya algorithm,
GWO algorithm and proposed algorithm are shown in Fig. 7. It can be observed
that all values are approximately equal. Graphical representation of the load is
useful for calculating the overall cost in the RTP signal.
The user waiting time (hour) is shown in Fig.8. The consumer satisfaction
level is measured in terms of waiting time. In our proposed scheme, user’s satis-
48 H. M. Faisal et al.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
2
4
6
8
10
12
Time (hour)
Per hour load (kWh)
Unscheduled
GWO
JGO
Jaya
Fig. 4. Per hour load
Unscheduled GWO JGO Jaya
0
1
2
3
4
5
6
7
8
PAR
Fig. 5. PAR
Unscheduled GWO JGO Jaya
0
200
400
600
800
1000
1200
1400
1600
Total cost (cent)
Fig. 6. Total cost
Towards Efficient Energy Management 49
Unscheduled GWO JGO Jaya
0
20
40
60
80
100
120
Total load (kWh)
Fig. 7. Total load
GWO JGO Jaya
0
1
2
3
4
5
6
Waiting time (hour)
Fig. 8. User waiting time
faction is the time limit a user waits for a particular appliance to turn on. So user
waiting time is inversely propositional to cost and PAR. We achieved minimal
user waiting time for high load and cost in DSM. Our proposed scheme achieved
minimum user waiting time however, cost and PAR values are compromised.
Proposed scheme of user waiting time is low as compared to GWO and Jaya.
While, increasing the cost and PAR, the waiting time increases, which is shown
in Fig. 8. Our proposed scheme tries to achieve the maximum trade off between
user waiting time and cost.
8 Conclusion
In this paper, we evaluate a load management problem in a smart home for
different electrical appliances. These appliances are scheduled using the meta
heuristics techniques according to their consumption pattern. We evaluate two
50 H. M. Faisal et al.
meta heuristic algorithms performance on the parameters of cost, PAR and user
comfort. Simulation result shows the tradeoff of cost and user waiting time.
Results show proposed updated population scheme is effective as compare to
GWO and Jaya in term of waiting time. In future, we will combine RES into SG
for PAR reduction.
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... Given its more advanced technological solutions, the smart power grid must be put into greater practice in order to attain the energy demands of the public as opposed to the current traditional grid which is unable to sustain public demands . Faisal et al. (2020) focused on DSM and a smart grids domain rather than household. The emphasis was largely on reducing the total cost for the end user based on PAR. ...
... The findings demonstrate a wider views and perceptual difference in scale of priority between the two groups. This partially supports Faisal et al. (2020) and Faure and Schleich (2018) claims regarding energy consumptiona reduction in peak loads results in less waiting time for the user which implies a reduction in energy consumption towards a decrease in carbon emissions. ...
Article
Purpose There are 29 million homes in the UK, accounting for 14% of the UK's energy consumption. This is given that UK has one of the highest water and energy demands in Europe which needs to be addressed according to the Committee on Climate Change (CCC). Smart homes technology holds a current perception that it is principally used by “tech-savvy” users with larger budgets. However, smart home technology can be used to control water, heat and energy in the entire house. This paper investigates how smart home technology could be effectively utilised to aid the UK government in meeting climate change targets and to mitigate the environmental impact of a home in use towards reducing carbon emissions. Design/methodology/approach Both primary and secondary data were sought to gain insight into the research problem. An epistemological approach to this research is to use interpretivism to analyse data gathered via a semi-structured survey. Two groups of participants were approached: (1) professionals who are deemed knowledgeable about smart home development and implementation and (2) users of smart home technology. A variety of open-ended questions were formulated, allowing participants to elaborate by exploring issues and providing detailed qualitative responses based on their experience in this area which were interpreted quantitatively for clearer analysis. Findings With fossil fuel reserves depleting, there is an urgency for renewable, low carbon energy sources to reduce the 5 tonnes annual carbon emissions from a UK household. This requires a multi-faceted and a multimethod approach, relying on the involvement of both the general public and the government in order to be effective. By advancing energy grids to make them more efficient and reliable, concomitant necessitates a drastic change in the way of life and philosophy of homeowners when contemplating a reduction of carbon emissions. If both parties are able to do so, the UK is more likely to reach its 2050 net-zero carbon goal. The presence of a smart meter within the household is equally pivotal. It has a positive effect of reducing the amount of carbon emissions and hence more need to be installed. Research limitations/implications Further research is needed using a larger study sample to achieve more accurate and acceptable generalisations about any future course of action. Further investigation on the specifics of smart technology within the UK household is also needed to reduce the energy consumption in order to meet net-zero carbon 2050 targets due to failures of legislation. Practical implications For smart homes manufacturers and suppliers, more emphasis should be placed to enhance compatibility and interoperability of appliances and devices using different platform and creating more user's friendly manuals supported by step-by-step visual to support homeowners in the light of the wealth of knowledge base generated over the past few years. For homeowners, more emphasis should be placed on creating online knowledge management platform easily accessible which provide virtual support and technical advice to home owners to deal with any operational and technical issues or IT glitches. Developing technical design online platform for built environment professionals on incorporating smart sensors and environmentally beneficial technology during early design and construction stages towards achieving low to zero carbon homes. Originality/value This paper bridges a significant gap in the body of knowledge in term of its scope, theoretical validity and practical applicability, highlighting the impact of using smart home technology on the environment. It provides an insight into how the UK government could utilise smart home technology in order to reduce its carbon emission by identifying the potential link between using smart home technology and environmental sustainability in tackling and mitigating climate change. The findings can be applied to other building types and has the potential to employ aspects of smart home technology in order to manage energy and water usage including but not limited to healthcare, commercial and industrial buildings.
... A well proposed new algorithm namely Genetic Algorithm Super-clustering (GASC) for scheduling appliances is done by using super clustering appliances and their working timing hours [3]. By scheduling the appliances of the smart home, the operation of these appliances can be shifted to off-peak hours and spread over a longer period of time that would in turn reduce the excessive energy consumed [4], [5]. Thus, optimizing the scheduling of appliances should greatly minimize the peak demand and electricity bills [6]. ...
Thesis
There is an exponential increase in the demand of the energy due to increasing electrical devices. This results in an increasing demand versus supply gap. Due to scarcity of fossil fuels (e.g., oil, gas and coal), global environmental concerns, the rise in demand and addition of multiple efficient power generating systems; reformation of the current energy system is imminent. Smart Grid (SG) is introduced to handle above mentioned challenges. Moreover, for the efficient use of SG, exact prediction about the future coming load is of great importance to the utility. It helps the utility to produce as much energy as needed. The objective of this work is to handle the load need in an adequate manner through coordination among appliances in a Smart Home (SH); and real-time information exchange between user and utility. In this research, we proposed two new home energy management systems that are using load shifting technique for demand side management to improve the energy consumption pattern in a SH. This work assesses the behavior of advising plans for real-time pricing and critical peak pricing schemes. Two different models for the scheduling of home appliances are proposed in this research. Both the models focuses on hourly scheduling of appliances in a SH while aiming daily electricity cost reduction, Peak to Average Ratio (PAR) minimization and user comfort maximization. Both these models are implemented at the electricity management controller level, installed in a SH within a SG architecture. In the first model the proposed scheme performance is compared with the crow search algorithm and Jaya algorithm. In the second model proposed scheme performance is compared with the strawberry algorithm and the earthworm optimization algorithm. The proposed schemes performance is assessed for PAR, user comfort and cost. Furthermore, we worked on forecasting load demand at the utility end, for exact required power generation. We used Extreme Gradient Boosting (XGBoost) for load prediction for the next 30 minutes using previous 7 days data recorded at the rate of 30 minutes time lag. For forecasting, in first step we use XGBoost for calculating feature importance, which is then used for feature selection. In next step we use XGBoost for forecasting the electricity load for single time lag, using the selected features. XGBoost perform extremely well for time series prediction with efficient computing time and memory resources usage. XGBoost based load prediction model performed very good for mean average percentage error metric.
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Demand side management (DSM) is one of the most challenging areas in smart grids, which provides multiple opportunities for residents to minimize electricity cost. In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization. Furthermore, a comparative analysis is also performed to show the legitimacy and productiveness of CSA and SA.
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Smart grid plays a significant role in decreasing of electricity consumption cost through Demand Side Management (DSM). Smart homes, a part of smart grid contributes a lot in minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to scheduling of home appliances. This scheduling problem is considered as an optimization problem. Meta-heuristic algorithms have attracted increasing attention in last few years for solving optimization problems. Hence, in this study we propose an efficient scheme in Home Energy Management System (HEMS) using Genetic Algorithm (GA) and Cuckoo search algorithm to solve optimization problem. The proposed scheme is implemented on a single smart home and a smart building; comprising of thirty smart homes. Real Time Pricing (RTP) signals are used in term of electricity cost estimation for both single smart home and a smart building. Experimental results demonstrate the extremely effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and Peak to Average Ratio (PAR) minimization. Moreover, our proposed scheme obtains the desired tradeoff between electricity cost and user waiting time.
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