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: firstname.lastname@example.org
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
Index Terms—Energy management, seasonal schedules, time of
use price, smart grid, deep neuro-fuzzy optimizer, takagi sugeno
fuzzy inference system, residential buildings.
Aggregation of ﬁne-grained information regarding electricity
is made possible via the use of smart meters for individual
consumers in any sector [
]. 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 [
], energy estimation [
various demand response programs [
], development of new
pricing tariffs [
], and users’ energy consumption patterns
]. These applications are evaluated through
the supervised or unsupervised learning methodologies, i.e.,
clustering, regression, classiﬁcation, etc. .
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. [
]. 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
]. One solution from the literature is on-line
building energy optimization technique based on reinforcement
learning which is presented in [
] 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 [
] 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.
], 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
]. 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 [
], 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
A new bluetooth based low energy oriented scheme is presented
to identify the sleeping duration of the devices in any home
]. 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 [
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
]. 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 [
]. 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 [
Authors in [
] have considered ﬂexible power requirement
and generation coordination issue. In [
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)
] 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 [
have presented the long term load forscasting using the
neural network and fuzzy logic with multilayer perceptron
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 [
] 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 [
]. 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 [
]. 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
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:
, 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,
denote the inputs to each
depicts the antecendent membership functions, whereas,
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
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
rule with the
summation of the ﬁring strength of all rules.
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
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)
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.
For evaluating the operation of these layers, Takagi sugeno
FIS is used; as mentioned above, for inferring the number of
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 [
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 [
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 .
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
Fig. 2: Training Results for Total Energy Utilization and
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
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
]. 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
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
Fig. 5: Tested Data Results for Total Energy Utilization and
TABLE I: Comparison of Performance Parameters.
Summer 15 1.0$ 8.0kWh 0.3kWh
Winter 10 4.0$ 12.5kWh 4.0kWh
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
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.
Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M.
E., Gibescu, M., and Slootweg, J. G. (2018). On-line building energy
optimization using deep reinforcement learning. IEEE Transactions on
Smart Grid. (Accepted).
Wang, Y., Chen, Q., Kang, C., Zhang, M., Wang, K., and Zhao, Y.
(2015). Load proﬁling and its application to demand response: A review.
Tsinghua Science and Technology, 20(2), 117-129.
Li, R., Li, F., and Smith, N. D. (2017). Load characterization and low-
order approximation for smart metering data in the spectral domain.
IEEE Transactions on Industrial Informatics, 13(3), 976-984.
Taieb, S. B., Huser, R., Hyndman, R. J., and Genton, M. G. (2016).
Forecasting uncertainty in electricity smart meter data by boosting
additive quantile regression. IEEE Transactions on Smart Grid, 7(5),
Zhang, P., Wu, X., Wang, X., and Bi, S. (2015). Short-term load
forecasting based on big data technologies. CSEE Journal of Power
and Energy Systems, 1(3), 59-67.
Hsiao, Y. H. (2015). Household electricity demand forecast based on
context information and user daily schedule analysis from meter data.
IEEE Transactions on Industrial Informatics, 11(1), 33-43.
Sijie, C. H. E. N., and Chen-Ching, L. I. U. (2017). From demand
response to transactive energy: state of the art. Journal of Modern Power
Systems and Clean Energy, 5(1), 10-19.
Wang, Y., Chen, Q., Kang, C., and Xia, Q. (2016). Clustering of electricity
consumption behavior dynamics toward big data applications. IEEE
transactions on smart grid, 7(5), 2437-2447.
Li, R., Wang, Z., Gu, C., Li, F., and Wu, H. (2016). A novel time-of-use
tariff design based on Gaussian Mixture Model. Applied energy, 162,
Chen, S., Love, H. A., and Liu, C. C. (2016). Optimal opt-in residential
time-of-use contract based on principal-agent theory. IEEE Transactions
on Power Systems, 31(6), 4415-4426.
 Chicco, G. (2016, October). Customer behaviour and data analytics. In
2016 International Conference and Exposition on Electrical and Power
Engineering (EPE) (pp. 771-779). IEEE.
Alam, M. R., St-Hilaire, M., and Kunz, T. (2016). Computational methods
for residential energy cost optimization in smart grids: A survey. ACM
Computing Surveys (CSUR), 49(1), 1-22.
Barbato, A., and Capone, A. (2014). Optimization models and methods
for demand-side management of residential users: A survey. Energies,
Loukarakis, E., Dent, C. J., and Bialek, J. W. (2016). Decentralized multi-
period economic dispatch for real-time ﬂexible demand management.
IEEE Transactions on Power Systems, 31(1), 672-684.
Mohsenian-Rad, A. H., and Leon-Garcia, A. (2010). Optimal residen-
tial load control with price prediction in real-time electricity pricing
environments. IEEE Trans. Smart Grid, 1(2), 120-133.
Paterakis, N. G., Erdin
, O., Pappi, I. N., Bakirtzis, A. G., and Catalao, J.
P. (2016). Coordinated operation of a neighborhood of smart households
comprising electric vehicles, energy storage and distributed generation.
IEEE Transactions on smart grid, 7(6), 2736-2747.
Vardakas, J. S., Zorba, N., and Verikoukis, C. V. (2015). A survey
on demand response programs in smart grids: Pricing methods and
optimization algorithms. IEEE Communications Surveys & Tutorials,
Hurtado, L. A., Mocanu, E., Nguyen, P. H., Gibescu, M., and Kling, W.
L. (2015, May). Comfort-constrained demand ﬂexibility management
for building aggregations using a decentralized approach. In 2015
International Conference on Smart Cities and Green ICT Systems
(SMARTGREENS) (pp. 1-10). IEEE.
Mocanu, E., Nguyen, P. H., Gibescu, M., Larsen, E. M., and Pinson, P.
(2016, June). Demand forecasting at low aggregation levels using factored
conditional restricted boltzmann machine. In 2016 Power Systems
Computation Conference (PSCC) (pp. 1-7). IEEE.
Ryu, S., Noh, J., and Kim, H. (2016). Deep neural network based demand
side short term load forecasting. Energies, 10(1), 1-20.
Shahgoshtasbi, D., and Jamshidi, M. M. (2014). A new intelligent
neuro–fuzzy paradigm for energy-efﬁcient homes. IEEE Systems Journal,
Naji, S., Shamshirband, S., Basser, H., Keivani, A., Alengaram, U.
J., Jumaat, M. Z., and Petkovi
c, D. (2016). Application of adaptive
neuro-fuzzy methodology for estimating building energy consumption.
Renewable and Sustainable Energy Reviews, 53, 1520-1528.
Lin, Y. H., and Tsai, M. S. (2014). Non-intrusive load monitoring by novel
neuro-fuzzy classiﬁcation considering uncertainties. IEEE Transactions
on Smart Grid, 5(5), 2376-2384.
Ghadi, Y. Y., Rasul, M. G., and Khan, M. M. K. (2016). Design and
development of advanced fuzzy logic controllers in smart buildings
for institutional buildings in subtropical Queensland. Renewable and
Sustainable Energy Reviews, 54, 738-744.
Collotta, M., and Pau, G. (2015). Bluetooth for Internet of Things: A fuzzy
approach to improve power management in smart homes. Computers &
Electrical Engineering, 44, 137-152.
Wui, Z., Zhangi, X. P., Brandt, J., Zhoui, S. Y., and Li J. N. (2015). Three
Control Approaches for Optimized Energy Flow With Home Energy
Management System. IEEE Power and Energy Technology Systems
Journal, 2(1), 21-31.
Ain, Q. U., Iqbal, S., Khan, S. A., Malik, A. W., Ahmad, I. and Javaid,
N. (2018). IoT Operating System Based Fuzzy Inference System for
Home Energy Management System in Smart Buildings. Sensors, 18(9),
Javaid, S., Javaid, N., Iqbal, S., Guizani, M., Almogren, A., and Alamri,
A. (2018). Energy Management with a World-wide Adaptive Thermostat
using Fuzzy Inference System. IEEE Access, 33489-33502.
Olatomiwa, L., Mekhilef, S., Ismail, M.S., Moghavvemi, M. (2016).
Energy management strategies in hybrid renewable energy systems: A
review. Renewable and Sustainable Energy Reviews 62, 821–835.
Paterakis, N. G., Erdin
, O., Pappi, I. N., Bakirtzis, A. G., and Catal
P. (2016). Coordinated operation of a neighborhood of smart households
comprising electric vehicles, energy storage and distributed generation.
IEEE Transactions on smart grid, 7(6), 2736-2747.
Kim, B. G., Zhang, Y., Van Der Schaar, M., and Lee, J. W. (2016).
Dynamic pricing and energy consumption scheduling with reinforcement
learning. IEEE Transactions on Smart Grid, 7(5), 2187-2198.
Mocanu, E., Nguyen, P. H., Gibescu, M., and Kling, W. L. (2016). Deep
learning for estimating building energy consumption. Sustainable Energy,
Grids and Networks, 6, 91-99.
Miranda, S. T. d., Abaide, A., Sperandio, M., Santos, M. M., Zanghi,
E. (2018). Application of artiﬁcial neural networks and fuzzy logic to
long-term load forecast considering the price elasticity of electricity
demand. Wiley, 1-17.
Semero, Y. K., Zhang, J., and Zheng, D. (2018). PV power forecasting
using an integrated GA-PSO-ANFIS approach and Gaussian process
regression based feature selection strategy. CSEE Journal of Power and
Energy Systems, 4(2), 210-218.
Azar, A. T. (2011). Adaptive Neuro-Fuzzy Systems. Electrical Communi-
cation & Electronics Systems Engineering department, Modern Science
and Arts University (MSA), City, Egypt.
Michael Sivak. Hot cities more sustainable than
cold ones, study says, (2013). Available online:
more-sustainable-than-cold-ones-study-says?lite,(accessed on 25 Jan.