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Towards Buildings Energy Management: Using

Seasonal Schedules Under Time of Use Pricing

Tariff via Deep Neuro-Fuzzy Optimizer

Sakeena Javaid1, Muhammad Abdullah1, Nadeem Javaid1,∗, Tanzeela Sultana1, Jawad Ahmed2, Norin Abdul Sattar3

1COMSATS University Islamabad, Islamabad 44000, Pakistan

2Capital University of Science and Technology, Isamabad, Pakistan

3Pounch University, Rawalakot, Azad Kashmir

∗Corresponding author: nadeemjavaidqau@gmail.com

Abstract—Management of increasing amount of the electricity

information provided by the smart meters is becoming more

valuable and a very challenging issue in modern era, especially

in residential sector for maintaining the records of consumers’

consumption patterns. It becomes the necessity of retailers and

utilities to provide the consumers more effective demand response

programs for handling the uncertainties of their consumption

patterns. In order to deal with the unceratian behaviours of

the consumers and their unprecedented high volume of data,

this work introduces the deep neuro-fuzzy optimizer for effective

load and cost optimization. Three premises parameters: energy

consumption, price and time of the day and two consequents

parameters: peak and cost reduction are used for the opti-

mization process of the optimizer. The dataset is taken from

the Pecan Street Incorporation site and Takagi Sugeno fuzzy

inference system is used for the evaluation of the rules developed

from the memebership functions of the parameters. Membership

Functions (MFs) are chosen as Guassian MFs for continuously

monitoring the consumers’ behaviours. Performance of this

proposed energy optimizer is validated through the simulations

which shows the robustness of optimizer in cost optimization and

energy efﬁciency.

Index Terms—Energy management, seasonal schedules, time of

use price, smart grid, deep neuro-fuzzy optimizer, takagi sugeno

fuzzy inference system, residential buildings.

I. INTRODUCTION

Aggregation of ﬁne-grained information regarding electricity

is made possible via the use of smart meters for individual

consumers in any sector [

1

]. This information is very helpful in

extracting the demands of consumers which is used for enhanc-

ing the services, upgrading the energy efﬁciency programs and

are useful for the improvement of the smart grid functionalities.

Various artiﬁcial intelligence methodologies have been pre-

sented for the extraction of the smart meter information which

are also applicable for the following applications: consumers

grouping and load proﬁling [

2

], energy estimation [

3

],[

4

],[

5

],

various demand response programs [

6

],[

7

], development of new

pricing tariffs [

8

],[

9

], and users’ energy consumption patterns

identiﬁcation [

10

]. These applications are evaluated through

the supervised or unsupervised learning methodologies, i.e.,

clustering, regression, classiﬁcation, etc. [11].

The proposed study has got motivation from the optimized

resource allocation for every individual consumer’s consumtion

patterns through the utility supply or the supply from the

renewable energy resources such as wind and solar. All

of these resources have stochastic nature, either they are

from consumption side or from generation side. Various

optimization techniques are already proposed in literature:

Particle Swarm Optimization (PSO), Binary PSO (PSO), fuzzy

logic, game theory, etc. [

12

]-[

18

]. However, these techiques

are not sufﬁcient to handle the large amount of the information

in the real databases. More precisely, these techniques take a

lot of time to manipulate the whole data in order to determine

the optimal solution which is not the optimized way to tackle.

In order to overcome this limitation, variety of deep learning

methodologies are presented to extract the users’ consumption

patterns for their efﬁcient control and management. The list

of these techniques is: auto-encoder, Convolutional Neural

Network (CNN), Recurrent Neural Network (RNN), Deep

Belief Network (DBN), Restricted Boltzman Machines (RBM)

and deep reinforcement learning.

Deep learning provides a lot of emergent techniques for

training deep neural networks through intelligent computation

capabilities [

19

],[

20

]. One solution from the literature is on-line

building energy optimization technique based on reinforcement

learning which is presented in [

1

] for analyzing the 4 years

data (approximately). However, it is based on the binary values

for maintaing its action and reward vectors which cannot

process the minute stochastic occurences in users’ consumption

patterns efﬁciently. In order to detect the small occurences in

the consumers’ behaviours, we present the deep neuro-fuzzy

logic technique. It uses fuzzy logic to identify the degree of

truth or uncertainty in power consumption patterns. Fuzzy logic

is based on the following steps: fuzziﬁcation, rule base, Fuzzy

Inference Systems (FISs) and defuzziﬁcation. In fuzziﬁcation,

it deﬁnes the linguistic variables for deﬁning the degrees of

occurences of certain parameter, i.e., low, medium or high. On

the basis of fuzziﬁcation; rule base is developed and then FIS is

used for evaluation of the rules. After the evaluation of the rules,

defuzziﬁcation is performed. Defuzziﬁcation gives the concrete

solution for the identiﬁed problems. On the other hand, neural

978-1-5386-7747-6/19/$31.00 ©2019 IEEE 1594

network provides efﬁcient training for optimization problems.

In our work, we are proposing the deep neuro-fuzzy optimizer

to handle the large amount of data (average hourly data of 5 ﬁve

years: 2012 to 2016) regarding electricity consumption patterns

in residential area. Upto the best of our knowledge, no such

similar work exists in this domain. Our major contributions

are enlisted below.

A. List of Contributions

There are following contributions which are described below:

•

We have proposed a deep neuro-fuzzy optimizer for energy

management in the residential buildings.

•

For the efﬁcient training and testing of the system, we have

used backpropogation algorithm with the deep nuero-fuzzy

optimizer for efﬁcient identiﬁcation of the consumers’

behaviours during each interval of the day.

•

The deep neuro-fuzzy optimizer is integrated with the

Takagi Sugeno FIS which is used for evaluation of the

rule base having 8 membership functions and 15 rules for

maintaining the energy consumption and cost of users’

schedules. Dataset for two seasons: summer and winter

is considered under Time of Use (TOU) pricing scheme.

•

This work is validated through the simulations which

proves that the proposed neuro-fuzzy optimizer gives

optimal cost and peak reduction.

II. LITERATURE REVIEW

Dariush et al. in [

21

] has presented a new intelligent EMS

(iEMS) for a smart home which is based on two subsystems:

a fuzzy subsystem and an intelligent lookup table. The ﬁrst

subsystem uses fuzzy rules and inputs which generates the

feasible results for the intelligent lookup table, whereas, second

subsystem is used for mapping the inputs to desired outputs

using the associated neural network architecture. An intelligent

lookup table considers three inputs which are derived from

the fuzzy subsystem, outside sensors, and feedback outputs.

This system considers that whatever trained in lookup table is

diverse according to the scenario and this system is sufﬁcient in

determining the best energy-efﬁcient solution in all scenarios.

In [

22

], another neuro-fuzzy approach is developed to predict

the energy utilization in the buildings by considering the

buildings’ physical properties, i.e., thickness and insulation

K-value. They have conducted more than 180 simulations

by considering several thickness and inculation values using

the EnergyPlus simulator. The Non-Intrusive Load Monitoring

(NILM) system is presented using the hybrid technique for

classiﬁcation [

23

]. This hybrid technique is based on the

combination of Fuzzy C-means clustering-piloting PSO with

neuro-fuzzy classiﬁcation. The household appliances can be

detected via electrical signatures in real scenario. Whereas,

certain anomalies are still found in case of load recongnition

which are resolved using the fuzzy logic. The proposed system

is validated in the real laboratory and home environments by

considering various uncertainties. In [

24

], a review of the fuzzy

logic controllers regarding the power saving services in smart

buildings of Australia is presented. In addition, authors have

also mentioned the drawbacks, conceptual foundations and

capabilities.

A new bluetooth based low energy oriented scheme is presented

to identify the sleeping duration of the devices in any home

area [

25

]. This scheme works on the battery level and their

throughput. In this work, authors have considered ﬁxed sleeping

duration of each appliance and it saves their time upto

30%. In addition, three energy management and controlling

techniques are introduced for household devices in [

26

]. Home

EMSs (HEMSs) track and schedule three types of devices:

1) heating, 2) storage, and 3) energy storage devices in

the considered scenario. Mixed Integer Linear Programming

(MILP), Continuous Relaxation (CR), and Fuzzy Logic are

used for the energy management along with three types of

approaches: heat-related fuzzy logic controller, task-related

fuzzy logic controller and fuzzy logic controller for battery.

Each technique is investigated for optimization of the cost,

computing resources and pratical scenarios. Fuzzy logic is used

for improving the consumers’ comfort standards by including

the humidity as an input from the consumers’ environment

[

27

]. They have also used the room temperature as a feedback

for the FIS in order to improve the energy efﬁciency. Authors

of this work claim that manual increment in deﬁning the rules

generates more error as compared to automatic rule generation.

For minimizing this error, they have proposed a new automatic

way of deﬁning the rule base via the combinatorial method. This

work is based on Mamdani and Sugeno FISs for inferrring

the rules from the rule base. The proposed technique has

maintained the thermal comfort by facilitating the consumers

with the ﬂexibility and efﬁciency in decision making.

A worldwide adoptive thermostat scheme is presented for

controlling the temperature setpoints of the residential area

using fuzzy logic [

28

]. Setpoint optimization has been done

for both the hot and cold regions of the world. Authors have

validated their approach using the datasets of Russia and Al-

Azizia cities. This scheme reduces the energy consumption upto

18% and 35% using two FISs: fuzzy mamdani inference system

and fuzzy sugeno inference system. However, this scheme

compromises the user comfort during peak hours because it

applies load curtailment during peak hours. A comprehensive

review of the standalone hybrid and MG-oriented hybrid

approaches regarding energy management is discussed in [

29

].

Authors in [

30

] have considered ﬂexible power requirement

and generation coordination issue. In [

31

], reinforcement

learning is used for the dynamic price management and

load scheduling. Using re-inforcement learning, consumers

learn without apriori knowledge and it helps in cost saving

through learning. Building energy prediction has performed

by Conditional Restricted Boltzmann Machine (CRBM) and

Factored Conditional Restricted Boltzmann Machine (FCRBM)

in [

32

] which is tested on the benchmark dataset of four

years. Performance of these schemes shows that they have

outperformed the previous schemes: ANN, Support Vector

Machine (SVM), and RNNs. In addition, authors in [

33

]

have presented the long term load forscasting using the

neural network and fuzzy logic with multilayer perceptron

1595

for obtaining the better training results.

III. SYS TE M MOD EL

The proposed system model is elaborated via the set of the

constraints and proposed energy optimizer descriptions. First,

system constraints are elaborated and then proposed optimizer

is described in detail.

A. System Constraints

The proposed learning algorithm has considered the follow-

ing mandatory constraints in order to guarantee the character-

istics of this deep neuro-fuzzy system:

•

Fuzzy sets for the deﬁned membership functions are

considered to stay in normalized form.

•

Fuzzy sets for membership functions have not assumed

to exchange their deﬁned boundaries.

•

The deﬁned fuzzy sets (or membership values) are

considered to overlap each other for every interval.

•

Summation of the membership degrees are considered

equal to 1.

B. Proposed Deep Neuro-Fuzzy Optimizer

In this system, we have considered the set of residential

buildings comprising of several consumers. The energy con-

sumtpion patterns of the consumers are recorded through a

dataset available on Pecan Street Incorporation site [

1

] for the

cost and peak reduction optimization during the 24 hours a

day. Deep neuro-fuzzy is the hybrid of deep neural network

and fuzzy logic. For the efﬁcient optimization of the cost and

peak reduction, we have applied the deep neuro-fuzzy optimier.

It is a hybrid approach where deep neural network has been

applied ﬁrst and then fuzzy logic is applied at second step

for computing the system objectives. The whole system is

comprised of following layers: input layer, number of hidden

layers for learning and validation and output layer. Input layer is

based on number of the inputs parameters and their fuzziﬁcation

values in a system [

34

]. Number of hidden layers are three:

rule layer, normalization and defuzziﬁcation layer. Output layer

is defuzziﬁcation layer. The most signiﬁcant parameters of this

system are: consequents and premises of the system. Here,

the number of the premise parameters include the following:

pricing tariff, load and time of the day, whereas, number of

the consequent parameters are: cost and peak reduction as

shown in Fig. 1.In this system, premises are foundations for

the membership functions in the fuzziﬁcation at input layer

which deﬁne the levels of the occurrences in the consumers’

behaviours or energy consumption patterns. The consequent

parameters are related to the defuzziﬁcation process. This

work addresses two major objectives: cost minimization and

peak reduction. The equations for the computing the objective

functions are taken from [

1

]. Neuro-fuzzy contains the takagi

sugeno FIS for rule base evaluation where we have used the

grid partitioning method for FIS rule generation purpose. This

is the default method available in the neuro-fuzzy system

which we have integrated with deep neuro-fuzzy optimizer.

The mathematical description of these parameters is described

below.

Each input or output parameter is mapped to the speciﬁc

entity or node (information processing unit) in the neuro-fuzzy

network for each layer. Degree to each input is assigned

between 0 and 1 as per criterion deﬁned by the fuzzy

system. Each entity in the ﬁrst layer is followed by an output

value. Assume that there are two premises:

u

and

v

and one

consequent:

w

, then these are represented in the next equation:

L1,j =µXj(u)or L1,j =µYj−2(v),∀j= 1,2,3,4.(1)

In Eq. 1,

u

and

v

denote the inputs to each

jth

entity,

µX

and

µYj−2

depicts the antecendent membership functions, whereas,

L1,j

represents the degree of membership. The membership

functions are represented by the bell shaped funtions (known

as guassian membership functions) which are assigned with

the maximum ”1” and minimum ”0” values.

µXj(u) = 1

1 + |u−sj

tj|2pi

(2)

Here,

pj

,

sj

, and

tj

shows the membership functions of the

premise parameters which are optimized through training.

Layer 2 is known as rule base layer which is used for describing

the set of rules. Every entity in this layer multiply the linguistic

veriables’ values to satisfy the degree of memberhship. The

product of membership variables values shows the ﬁring

strength of the rule as described by the equation below.

L2,j =ωj=µXj(u)µYj−2(v),∀j= 1,2.(3)

Layer 3 deals with normalization where every entity com-

putes the ratio of the ﬁring strength of

jth

rule with the

summation of the ﬁring strength of all rules.

ωj

represents

the generic network parameter: weight. The result of every

rule is then normalized via ﬁring strength of the rule which is

described as under.

L3,j = ¯ωj=ωj

ω1+ω2,∀j= 1,2.(4)

Fourth layer is the defuzziﬁcation layer where each rule’s

consequents are computed to represent their overall effect on

the output. This phenomenon is mathematically described by

the equation below.

L4,j = ¯ωjzi= ¯ωj(aju+bjv+cj),∀j= 1,2.(5)

Here,

a

,

b

, and

c

are the consequent parameters set. Af-

terwards, last layer is considered as summation layer which

computes the summation of the previous layers outcomes. Next

equation describes the process of the ﬁnal result computation.

L5,j =X

j

¯ωjzj=Pjωjzj

Pjωj

(6)

For evaluating the operation of these layers, Takagi sugeno

FIS is used; as mentioned above, for inferring the number of

1596

Fig. 1: System Model for the Optimal Energy Consumption.

rules deﬁned by the IF-THEN statements using the linguistic

membership variables of the antencendents and consequents.

These parameters are initially assigned with the random values,

then they are tunned through the training algorithm for best

values optimization. In this case, backpropogation algorithm

is used for training of the system parameters. In this system,

number of hidden layers are used for the making the system

training efﬁcient for the large set of data. Three types of

appliances are considered in this work: time scaling, time

shifting and time shifting and scaling appliances. Time scaling

appliances are air conditioners, which cloud be switched-on

or off for any time interval. Secondly, we consider the time

shifting appliances, like dishwashers. These appliances are

deferrable appliances and can be shifted to the other time

intervals. Thirdly, we consider the EVs as time shifting and

scaling appliances of the building.

IV. SIMULATION RESULTS

In this section, description of the dataset is explained ﬁrst

and then simulation results of the proposed optimizer with

respect to price and peak load reduction are discussed. Two

scenarios are discussed in the simulations: peak load and cost

reduction for summer season and peak load and cost reduction

for winter season.

A. Dataset and System Parameters Description

We have used the dataset of 5 years (2012 to 2016) regarding

energy consumption of residential buildings of city Austin

for conducting the simulations of proposed optimizer. For

determining the patterns of the residential users, we have

collected the average hourly data of each year. This data is

recorded from the Pecan Street Incopration site [

1

]. Firstly,

75% of dataset is categorised for the training of the network.

Secondly, 25% of the dataset is used for the training of the

network. Other system parameters are taken from [

35

]. Pricing

tariff used in this work is TOU for the residential area which

is developed according to the consumers’ living patterns in

the city of Austin. Price tariff is desinged for both summer

and winter seasons, where summer tariff includes the on-peak,

mid-peak and off-peak tariffs, and winter tariff is only having

the off-peak and mid-peak tariff [1].

B. Peak Load and Cost Reduction for Summer Season

The total energy consumption and reduction is shown in

Fig. 2(a) using our proposed scheme, where data is collected

for 24 hours and it is categorised for testing and training as

mentioned above. Initial 18 hours data is used for training

and the remaining six hours data is used for testing. The

training phase of the proposed scheme is shown for initial

18 samples which shows the total observed consumption and

reduction patterns. During the training phase, maximum 8

kWh conumsption is observed during any hour of the day,

whereas, 0.3 kWh reduction is observed per hour. The reason

is that the use of ANN tranining and evaluation of fuzzy

rules through takagi sugeno FIS helps in efﬁcient training and

evaluation. Mean Square Error (MSE), Root Mean Square Error

(RMSE) are introduced during the cost computation which

shows robustness of the system by considering the uncertainties

as shown in Fig. 2(b). The maximum cost procured in any

hour during training phase is equal to 1$. Takagi sugeno FIS

uses guassian membership functions which uses the mean and

standard deviation as inputs for computing the number of rules

based on these parameters and are displayed in Fig. 2(c).

After training, testing is performed for the remaining 25%

of the selected dataset considering the remaining 6 hours of the

1597

Fig. 2: Training Results for Total Energy Utilization and

Reduction.

day. Fig. 3(a) shows the testing results for the remaining 6 hours

which shows that the maximum 1.6 kWh energy is consumed

per hour and 0.2 kWh energy is reduced per hour. Fig. 3(b)

shows the cost considering the MSE and RMSE which shows

the robustness of sytem in uncertainity-oriented environment.

During the testing phase, the obtained cost is shown below

0.15$ which proves that system has been trained intelligently.

Fig. 3(c) shows the participation of membership fucntions using

the mean and standard deviation of the membership functions

as an input.

Fig. 3: Testing Results for Total Energy Utilization and

Reduction.

C. Peak and Cost Reduction for Winter Season

In this section, the proposed optimizer is tested using the

winter TOU pricing tariff. For training phase, again dataset is

categorised for 75% of the total dataset (similar to the summer

season scenario). In this phase, initial 18 hours of the day are

considered where maximum 10 kWh consumption is observed

during any hour of the day. Maximum 4 kWh peak reduction

is observed because appliances used in winter season consume

more load as compared to the appliances used in summer season

[

36

]. These appliances are having high power rating and if

there are load reduction strategies applied, more reduction is

also observed. Fig. 4(a) shows the training results for energy

consumption and reduction results, whereas, Fig. 4(b) and Fig.

4(c) show the cost and number of rules’ participation of the

system. Cost is more high as maximum 4 kWh is observed

during any hour because of the heavy load appliances used in

the winter season.

Fig. 4: Training Data Results for Total Energy Utilization and

Reduction.

After training phase, testing is performed for the last six

hours of the day. In Fig. 5(a), maximum 3.5 kWh consumption

is observed and there is no reduction observed there. Surplus

power is consumed in the night hours because residents are

staying at home and heating systems are on most of the time

while consumers stay at home. However, they mostly shift their

heating systems to the storage devices or renewable energy

systems. At night time, prices are normally low because of the

off-peak hours. So, the cost obtained is very low as displayed

in Fig. 5(b). Fig. 5(c) shows the number of rules used in testing

phase for cost computation, energy consumption and energy

reduction processes.

Fig. 5: Tested Data Results for Total Energy Utilization and

Reduction.

TABLE I: Comparison of Performance Parameters.

Season

Number

of Rules

Cost

Energy

Consumption

Peak Reduction

Summer 15 1.0$ 8.0kWh 0.3kWh

Winter 10 4.0$ 12.5kWh 4.0kWh

V. CONCLUSION

In this work, a deep neuro-fuzzy optimizer has been proposed

for the efﬁcient optimization of the cost and energy for two

seasons using the large dataset (2012 to 2016). This optimizer

1598

combines the functionalities of fuzzy logic and deep neural

network. Fuzzy logic deals with the handling of uncertainties

of data, whereas, neural network helps in efﬁcient training by

tuning the FIS parameters and computation of large scale data.

This optimizer has been used for the testing and training data of

5 years. Guassian membership functions are used for efﬁcient

monitoring of the system’s states for every hour. From the

simulation results, it provides efﬁciency in energy reduction for

the consumers using the TOU tariffs’ rates for both summer and

winter seasons. MSE and RMSE metrices have been introduced

for computing the cost of the system in current scenarios

for proving the system robustness. It is concluded from the

simulation results that using the same set of rules for the same

parameters’ set do not improve the performance; however, by

altering the number of epochs and error tolerance, system

performance can be improved.

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