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An Enhanced Very Short-Term Load Forecasting Scheme Based on Activation Function

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In this paper, we proposed a framework for accurate load forecasting which consists of two stage processes; feature engineering and classification. Feature engineering consists of feature selection and extraction. Relevant features are selected by combining Decision Tree (DT) and Recursive Feature Elimination (RFE) techniques. Moreover, Linear Discriminant Analysis (LDA) technique is used to further improve the selected features in terms of redundancy and dimensionality reduction. To forecast the electricity load, an improved feedforward multilayer perceptron classifier is applied. Half a day ahead forecasting experiment is conducted by using the proposed framework. At the end, forecasting performance is examined by using Root Mean Square Error, Mean Absolute Error, Mean Square Error and Mean Absolute Percentage Error. Simulation results show higher accuracy of our proposed scheme with 1.397% as compared to the existing scheme.
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1
An Enhanced Very Short-Term Load Forecasting
Scheme Based on Activation Function
Adamu Sani Yahaya1, Nadeem Javaid1,, Kamran Latif2and Amjad Rehman3
1COMSATS University Islamabad, Islamabad 44000, Pakistan
2National Institute of Electronics, Islamabad 44000, Pakistan
3MIS Department COBA, Al Yamamah University, Riyadh 11512, Saudi Arabia
nadeemjavaidqau@gmail.com
Abstract—In this paper, we proposed a framework for ac-
curate load forecasting which consists of two stage processes;
feature engineering and classification. Feature engineering con-
sists of feature selection and extraction. Relevant features are
selected by combining Decision Tree (DT) and Recursive Feature
Elimination (RFE) techniques. Moreover, Linear Discriminant
Analysis (LDA) technique is used to further improve the selected
features in terms of redundancy and dimensionality reduction. To
forecast the electricity load, an improved feedforward multilayer
perceptron classifier is applied. Half a day ahead forecasting
experiment is conducted by using the proposed framework. At the
end, forecasting performance is examined by using Root Mean
Square Error, Mean Absolute Error, Mean Square Error and
Mean Absolute Percentage Error. Simulation results show higher
accuracy of our proposed scheme with 1.397% as compared to
the existing scheme.
Index Terms—Electricity load forecasting, Feature selection,
Features extraction, Feedforward multilayer perceptron, Accu-
racy.
I. INTRODUCTION
Intelligent power grid, also known as smart grid (SG) is a
power system that efficiently manages consumption, distribu-
tion and generation of energy through advanced technologies.
The communication between the utilities and consumers in
smart grid is done in two-way peer-peer i.e., from utility to
consumer and vice versa [1]. In this world, utilization of
energy is necessary and valuable asset. Increase in energy
consumptions from various sectors such as commercial, resi-
dential, industrial and transportation escalated burden on tradi-
tional power grids. Increase in load consumption forces utility
to migrate from traditional power grid to smart grid. With
the implementation of different techniques on utility, power
grid and demand side, Smart grid manages the consumption,
distribution and generation.
In smart grid, data analysis and information communication
technology are in use into every angle of the energy system
such as energy distribution, generation, transmission and also
in appliances of consumer. Smart grid technology is rapidly
increasing in order to meet the increasing demand for energy
delivering in a flexible, robust and in cost effective manner. As
a result of the increase in data generated from smart grid, data
analysis becomes the topic of discussion. Valuable information
from the huge amount of data generated from smart grid can
easily be extracted by data analytics techniques. The valuable
TABLE I: List of Symbols
Symbols Description
f(x)improved Improved activations function
f(x)Logistic sigmoid function
g(x)Rectify linear unit function
PiThe forecasted electricity load value
RiThe actual electricity load value
TABLE II: Abbreviation Table
Abbreviation Description
AC AutoCorrelation
CFS Correlation-Based Selection
DE Differential evolution
DT Decision Tree
EMD Empirical mode decomposition
ELM Extreme learning machine
FOA Fruit Fly Optimization
FS Feature Selection
GPSO Global Best Particle Swam Optimization
GCA Grey Correlation Analysis
GWO Grey Wolf Optimization
IMF Intrinsic mode functions
KELM kernel extreme learning machine
KPCA Principle Component Analysis and Kernel function
LDA Linear Discriminant Analysis
LSTM Long Short-Term Memory
LTLF Long-Term Load Forecasting
MAE Mean Absolute Error
MAPE Mean Absolute Percentage Error
MTLF Medium-Term Load Forecasting
MSE Mean Square Error
PSO (SDPSO) new switching delayed PSO.
QLD New Queensland
RBFNN Radial basis function neural network
RF Random Forest
RF RRelief
RFE Recursive Feature Elimination
RMSE Root Mean Square Error
SDA Stacked denoising autoencoder
SG Smart Grid
STLF Short-Term Load Forecasting
SVM Support Vector Machine
VSTLF Very Short-Term Load Forecasting
information can be use in algorithms development and scheme
designed.
Machine learning and pattern recognition are among the
data analytics techniques that been developed and adopted
especially in the area of load and price forecasting, data
mining of consumer behaviors and fault detection in energy
networks [2]. The research in electricity load forecasting is
not new, is begin in earlier 1965 [3]. Forecasting electricity
2019 International Conference on Computer and Information Sciences (2019 ICCIS) 978-1-5386-8125-1 c
2019 IEEE
2
load accurately is paramount important in decision-making
process for economic load dispatch, power unit commitment,
contingency, power system operation, scheduling, security and
so on [3], [4].
Proper decision may results in minimization of cost of elec-
tricity and power loss [5]. According to [4] one percent (1%)
reduction of mean absolute percentage error in load forecasting
saved 10,000Mw electricity energy which may lead to saving
approximately $1.6 million per year. With these problems,
researchers paid more attention on how to resolve the power
scheduling problem. Many techniques had been used to opti-
mize power scheduling problem [6]–[10]. However, a precise
load forecasting model is required to properly plan on how
to effectively managed power grid resources. Redundancy and
nonlinearity in the historical electricity load data causes huge
challenges in load forecasting. The role of electricity load
forecasting is increasing in electricity generation, system oper-
ation, transmission and storage while forecasting accurately is
becoming a major concern in the management [11]. Therefore,
many researchers focused on how to promote the accuracy and
robustness of the electricity load forecasting. According to [3],
[12] and [13] electricity forecasting horizons are categorized
into Very Short-Term Load Forecasting (VSTLF), Short-Term
Load Forecasting (STLF), Medium-Term Load Forcasting
(MTLF) and Long-Term Load Forecasting (LTLF) as shown
in Table III. In this research, very short-term load forecasting
horizon will be the main focus. Modified feedforward mul-
tilayer perceptron will be used as classifier to forecast the
electricity load.
A. Motivation
A lot of researches had been conducted in electricity load
forecasting as in [3], [11]–[14]. Although, optimal result from
each model is obtained. However, forecasting accuracy of load
and price needs further improvement. As consequence, this
motivated us to propose a model that provides better accuracy
as well as to improve the efficiency of the existing models in
literature.
B. Problem Statement
In [13], recurrent deep neural network and feedforward deep
neural network models performance are compared on the basis
of accuracy and computational performance in which they con-
sider short-term forecasting. Also [14], Global Best Particle
Swam Optimization (GPSO) is proposed to optimized Neural
Network bias value in training processes which decrease the
error and increase accuracy.
In this paper, very short-term load forecasting is considered
to predict the variations in load and activation function is
modified to increase accuracy and decrease error which are
not considered in [13], [14]. Although, the dataset used in
this research is not the same as that of the benchmark models,
However a reasonable result is obtained. The neural network
is improved by combining logistics sigmoid and rectify linear
unit activation functions.
C. Contribution
The main contributions of this research is as follows:
Proposing an improved electricity load forecasting sys-
tem.
Combination of Decision tree and Recursive feature elim-
ination are used in features selection to find the most
relevant features.
To reduce features redundancy and dimensionality, linear
discriminant analysis is applied.
Activation function of Feedforward multilayer perceptron
is improved by combining logistics sigmoid and rectify
linear unit functions which enhanced the accuracy and
decreases the errors in the forecasted result.
The term prediction and forecasting are alternatively used
throughout this paper. The remaining part of this paper is
planned as follows: Section 2 contained the related works,
section 3 gives details of the techniques (Methodology) used
to build up the model and section 4 describes system model,
dataset and their experimental setup. Section 5 describes the
performance estimation while the simulations result obtained
are discussed in section 6. Finally, The conclusion of the
research is discussed in Section 7.
II. RE LATE D WORK
Lots of researches had been done in STLF in order to
improve forecasting accuracy. Kalman filtering, linear regres-
sion methods, exponential smoothing, gray forecasting and
ARIMA [16]–[18] were proposed in the primal stage. These
forecasting models are good in forecasting linear problem
however are not sufficient when processing more complex
non-linear load problem forecasting. Due to the limitations
of these models, accurate forecasting cannot be achieved. The
aforementioned models are based on mathematical statistics.
Automated and more intelligent artificial neural network tech-
niques were developed to overcome the complex relationship
and non-linearity in electricity time series. When an ANN was
developed, fewer parameters were needed to step up the model.
This directly affects the forecasting performance. This reason
influence the researchers to come up with hybrid models [19]
which leads to the addition of numerous smart optimizations
techniques like Fruit Fly Optimization Algorithm (FOA), Grey
Wolf Optimization Algorithm (GWO) e.tc [20], [21]. In [5]
cuckoo search is incorporated with Support Vector Machine
(SVM) to improve its parameters. The performance of the
model significantly increase in the experimental results. In [22]
New Switching Delayed PSO (SDPSO) is used to enhance
the Extreme Learning Machine (ELM) techniques. The exper-
imental result of the model outperforms Radial Basis Function
Neural Network (RBFNN) model. However, experiment focus
only in STLF. Finding shows that combination of two or more
models can obtain better forecasting result than the single
ANN model. A robust combination model is proposed by
many researchers to integrate more than two forecasting mod-
els [23]. This approach balance the distribution of forecasting
risk and gain benefits of individual models.
Some researchers observed that best forecasting result is
not guarantee with models that has unavoidable deficiency.
3
TABLE III: Forecasting Horizons
S/N Category Period range Aim
1 VSTLF The range is between Few minutes, 30 minutes to approximately one day. To control and adjust demand load and price in real-time
2 STLF Started with one day to a week and upto a month To makes a real-time plan for optimal generator unit commitment
and economic dispatch
3 MTLF start with one month upto approximately one year To maintain the balance between generation and demand for few
months to one year
4 LTLF More than one year To plan for the future electricity network conditions
As a result of that combining two or more forecasting model
is proposed to resolve the limitations. A hybrid model of
Boosting algorithm and a multistep forecast are combined to
produce accurate forecasting results in electricity market [24].
In [25], Auto Regressive Moving average and Self-adapting
Particle Swarm Optimization improve the hybrid combina-
tion of the Kernel Extreme Learning Machine (KELM) and
wavelet transform. The result produce by the model was
remarkable. however, computational time is not considered.
A short-term electricity price was forecasted in [26] using
Stacked Denoising Autoencoder (SDA). The result of the
model was compared with multivariate adaptive regression
splines, classical NN, SVM and least absolute shrinkage and
selection operators and better result is obtained. The price
forecasting accuracy decreases in [27] when forecasting wider
range i.e. weeks, month. Four deep learning techniques were
used to predict the electricity price. The techniques used are
DNN, LSTM-DNN, GRU-DNN and CNN model.
Features Selection (FS) contributed immensely in machine
learning to establish a powerful and reliable model. Feature
selection is a process whereby a set of important features with
high correlation with output are selected [28]. Researchers
used different set of feature selection method. For example, in
[29] four feature selection techniques: RRelief (RF), Mutual
Information, Correlation-Based Selection (CFS), and Auto-
correlation (AC) were evaluated. The corresponding experi-
mental results show that AC-NN and RF-NN gives a better
performance. In [30] Grasshopper and evolutionary population
dynamics techniques are used to select more relevant features.
This method performed very well when handling a larger set
of features and on the other hand, its performance reduced
drastically with a smaller set of input features. Forecasting
performance increasing with more relevant features and vice
versa. In [31], K-means and gradient boosting-based weighted
techniques are used to find the similarities between the actual
and predicated days. Empirical Mode Decomposition (EMD),
decompose the similar days to residual and several Intrinsic
Mode Functions (IMFs) and LSTM were used to forecast each
decomposed result. New forcasting model is established in
[32]. a hybrid feature selection is proposed by combining
Relief-F algorithm and Random Forest (RF) together with
Grey Correlation Analysis (GCA) in order to minimized
the redundancy. Principle Component Analysis and Kernel
function (KPCA) are integrated to reduced dimensionality. In
the end, SVM is improved by using differential evolution (DE)
to forecast the electricity price.
III. METHODOLOGY
A. Decision Tree
Decision tree aim to find the purest child possible nodes
with minimum split in order to classified the instances. In this
model “Information gain” is used as the attribute selection
measures to find purest child nodes.
Algorithm 1 Decision Tree
1: S, where S= set of classified instances
2: Require:S6=, numattribute > 0
3: procedure DECISION TREE :
4: Repeat:
5: maxGain 0
6: splitA null
7: eEntropy(attributes)
8: for all a in S do
9: gain InformationGain(a,e)
10: if gain >maxGain then
11: maxGain gain
12: splitA a
13: end if
14: end for
15: Partition(S, splitA)
16: Untill all partition processed
B. Recursive Feature Elimination
Description on how iterative Recursive Feature Elimination
(RFE) works is provided as follows:
1) The classifier will be trained.
2) The ranking criterion for all features will be calculated.
3) Eliminate all the features with the lowest ranking crite-
rion.
The algorithm of SVM-RFE is described in algorithm 2
Algorithm 2 Recursive Feature Elimination
1: procedure RFE:
2: classifier training :α=train SV M (x, y)
3: weight vector is computed of the demension legth :
4: w=Pkαkxkyk
5: ranking criterias is computed : ci= (wi)2
6: Find the feature with the smallest ranking criteria :
7: f=argmain(c)
8: Eliminate feature with c=f
The stop condition can be based on the number of features
or the classification accuracy.
4
C. Linear Discriminant Analysis
The pseudocode for LDA is depicted in Algorithm 3. Where
1nis a vector of all ones and 1nR, with the appropriate
dimension for each class i = 1, 2. Linear Discriminant Analysis
proceeds to compute the in-between and within class scatter
matrices, B and S, after The data is dividing into two groups
G1 and G2. The best LD vector is obtained as the dominant
eigenvector of S1B.
Algorithm 3 Linear Discriminant Analysis
1: procedure LDAG={(xT
i, yi)}n
i=1:
2: specific-class subsets :
3: Gi={(xT
j|yj=ci, j = 1, .., n}, i = 1,2
4: class means :
5: βi=mean(Gi), i = 1,2
6: scatter in-between matrix class :
7: B= (β1β2)(β1β2)T
8: Centeric matrices class :
9: zi=Di1nβT
i, i = 1,2
10: scatter Class matrices :
11: Pi=zT
izn, i = 1,2
12: class within scatter matrix :
13: P=P1+P2, i = 1,2
14: computed eigenvector is obtained :
15: λ1,w=eigen(P1B)
D. Feed Forward Neural Network
The Multilayer feedforward NN is divided into three layers.
The layers are input layer, hidden layers and output layers
(Figure 1) [14].
Furthermore, relevant features with lowest error forecast
were used as input of feedforward neural networks. In this
research, an enhanced multilayer perceptron was used to
train the network. The activation function of the model is
improved by combining the logistic sigmoid function (equ.
1) and rectify linear unit (equ. 2). Both activations function
happened to produced good result compared to the remaining
functions. The new activation function f(x)improved is used
to improve accuracy of electricity load forecasting.
f(x) = 1
1 + ex(1)
g(x) = (0, if x<0
1, if x0(2)
Combining equation 1 and 2 we have the improved function
f(x)improved = 2(f(x)g(x)) (3)
where the constant value 2 is a scaling factor
Fig. 1: Feedforward Neural Network [15]
IV. SYS TE M MO DE L
Split load dataset and apply
normalization
Historical
electrical load
Feature selection using decision
tree and recursive feature
elimination
Training feature vectors
Feature extraction
using linear
discriminant analysis
Prediction algorithm using
enhanced FF-NN
Prediction result
Fig. 2: Proposed System Model
The electricity load demand data set was collected from
New Queensland (QLD) Australia [33]. The collected load
data are used to verify the authenticity of the proposed model
performance. The load data are sampled for every half an
hour, which means there are 336 observations per week and 48
observations per day. The data set is covered from 11-10-2018
21:00–14-10-2018 4:00 in QLD.
The collected data are used as input variable of the forecast
model. After carrying out some preprocessing, predictive anal-
ysis is performed with enhanced feedforward neural network
method as shown in figure 2.
In the preprocessing step, data is normalized and then
divided into three parts: train, test and validation. Following
the preprocessing, the data is then given to hybrid features
5
selection. The hybrid features selection is the combination of
decision tree and recursive feature elimination, which are used
to select the relevant features. The features selected by the
hybrid feature selection are assumed to have fewer irrelevant
features; however, dimensionality and redundancy are further
reduced to improve the forecasting accuracy. Linear discrimi-
nant analysis (LDA) is applied to remove the redundancy and
reduce dimensionality of the dataset.
Low weighted and less redundant features have been filtered
after the processes with feature selector and feature extraction.
Finally, the filtered data will be sent to the enhanced feed
forward neural network to forecast the electric load.
V. PERFORMANCE ESTIMATION
In order to measure the forecasting performance of the
model, four standard evaluating metrics are used: Root
Mean Square Error (RMAE),Mean Absolute Percentage Error
(MAPE), Mean Absolute Error (MAE) and Mean Square Error
(MSE). They are defined in equations (1)-(4) [3], [12], [13].
M AP E =1
w
w
X
i=1
|RiPi
Ai
|(4)
MAE =1
w
w
X
i=1
|RiPi|(5)
RM SE =v
u
u
t
w
X
i=1
(RiPi)2
Ri
(6)
MSE =
w
X
i=1
(RiPi)2
Ri
(7)
where Piis the forecasted electric load value at point i,Riis
the actual electric load value at point i;wis the total number
of data; .
VI. SIMULATIONS RESU LT
Numerical results are provided in this section to validate the
new forecasting model. Figure 4 depicted the normalized loads
of QLD for three days (half an hour basis). The normalization
graph shows different variations across the hours. In the
implementation, data are divided in the ratio 75:25. Where
training data has 41.5 hours and testing data has 14 hours. The
processed data from feature selection and extraction techniques
are then forwarded to the forecasting engine for predictions
as shown in figure 3. The data of features selection and
dimensionality reduction is given in Table IV.
TABLE IV: Attribute of Data
S/N Features Status
1 Date R
2 Net Import (MW) R
3 Spot Price ($/MWh) S
4 Scheduled Generation (MW) S
5 Semi Scheduled Generation
(MW)
S
6 Type R
NOTE: The ‘S’ means that the feature is relevant and should
be selected, the ‘R’ means the feature is irrelevant and should
be rejected.
Fig. 3: Comparison of the Forecasted and Actual Load in QLD.
Fig. 4: Normalized load of QLD from 11-10-2018 21:00–14-
10-2018 4:00.
Fig. 5: Comparison of Performances Evaluators.
TABLE V: Errors evaluation metrics for the QLD market for
the days 11-10-2018 21:00–14-10-2018 4:00
Method MSE MAE RMSE MAPE
Existing
Model
24.505 14.289 17.502 3.745
New Model 10.455 9.05 11.432 2.366
6
The actual and forecasted loads for the enhanced and existing
model of the QLD is presented in figure 3. In this figure,
blue curves represent the actual load, the red curve is the
forecasted load for the existing model while the yellow curve
is for the enhanced model. As we can see from figure 3, the
performance of the enhanced model shows good result, since
the load curve of the enhanced model is more closely to the
actual value as compared to the existing model. Error analysis
performance result can be obtained from table V. Figure 5
shows the comparison within performance evaluators. Table
V records four forecasting errors about the proposed and
existing model in the experiment. As shown in table V, the
result of performance apparently shows that the proposed
model is reasonably good because it has the lowest error in
MAPE, MAE, MAE, RMSE and MSE. For example, Value
of MAPE is 2.366%, which is apparently smaller than that
of the existing model. This indicates that the new model
outperforms its counterpart model.
VII. CONCLUSION
This research proposed a modified electricity load forecasting
model by improving the activation function of the existing
model. The corresponding results of the experiment show that
the new model outperforms the existing one with 1.397%. Fea-
ture selection and extraction are used to reduce dimensionality
and redundancy in order to give a more relevant features to
predictor.
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... Therefore, improvements in the literature are strongly needed. To manage energy efficiently, some research works use machine learning [112][113][114], and deep learning [115][116][117][118][119][120] techniques. Other works enhance the techniques using game theory optimization algorithms [121,122], while some use heuristic [123][124][125][126][127][128][129] and metaheuris-tic [130][131][132][133][134] algorithms. ...
Research Proposal
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The rapid deployment of Electric Vehicles (EVs) and usage of renewable energy in day-to-day activities of energy consumers have contributed toward the development of a greener smart community. However, load balancing problems, security threats, privacy leakages, and lack of incentive mechanisms remain unresolved in energy systems. Many approaches have been used in the literature to solve the aforementioned challenges. However, these approaches are not sufficient to obtain satisfactory results because of the resource and time-intensiveness of the primitive cryptographic executions on the network devices. In most cases, energy trading systems manage transactions using a centralized approach. This approach increases the risk of a single point of failure and overall system cost. In this study, a blockchain based Local Energy Market (LEM) model considering Home Energy Management (HEM) system and demurrage mechanism is proposed to tackle the issue of a single point of failure in the energy trading system. It allows both the prosumers and consumers to optimize their energy consumption and minimize electricity costs. This model also allows end-users to shift their load to off-peak hours and to use cheap energy from the LEM. On the other hand, users’ privacy leakages are still not solved in blockchain and can limit its usage in many applications. This research also proposes a blockchain based distributed matching and privacy-preservation model that uses a reputation system for both residential homes and EVs to preserve users’ privacy and efficiently allocate energy. A starvation free energy allocation policy is presented in the model. In addition, a charging forecasting scheme for EVs is introduced that allows users to plan and manage their intermittent EVs’ charging. Partial homomorphic encryption based on a reputation system is used to hide the EVs users’ whereabouts. Identity Based Encryption (ID Based encryption) technique is incorporated in the model to preserve the users’ information privacy in the blockchain. Another bottleneck in the energy trading systems is to perform efficient and privacy-preserving transactions. Therefore, an efficient and secure energy trading model leveraging contract theory, consortium blockchain, and a reputation system is proposed. Firstly, a secure energy trading mechanism based on consortium blockchain technology is developed. 1 Then, an efficient contract theory based incentive mechanism considering the information asymmetry scenario is introduced. Afterwards, a reputation system is integrated to improve transaction confirmation latency and block creation. Next, a shortest route and distance algorithm is implemented in order to reduce the traveling distance and energy consumption by the EVs during energy trading. Cheating attacks launched by both buyers and sellers are also issues, which are still not resolved. Thus, a mutual-verifiable fairness mechanism during energy trading based on timed commitment is presented. Proof-of-Energy Reputation Generation (PoERG) and Proof-of Energy Reputation Consumption (PoERC) consensus mechanisms are proposed to solve the high computational cost and huge monetary investment issues created by Proof-of- Work (PoW) and Proof-of-Stake (PoS) existing mechanisms. The mechanisms are developed based on reputation where energy trading transactions are audited, validated, and added into blocks of a blockchain. In order to protect the proposed model from impersonation attacks and minimize malicious validators, a two-stage peer-to-peer secure energy trading model based on blockchain is proposed. The proposed model has two layers: a mutual authentication process layer, and a secure and privacy-preserving energy trading layer. Afterwards, an incentivepunishment algorithm is introduced to motivate energy prosumers to contribute more energy in the proposed model. Next, a dynamic contract theory based supply-demand ratio pricing scheme is proposed. The purpose of the proposed pricing scheme is to solve the issues associated with the existing pricing scheme. Also, to preserve the privacy of the actual energy consumption behavior of the trading participants.
... Therefore, improvements in the literature are strongly needed. To manage energy efficiently, some research works use machine learning [143,144,145], and deep learning [146,147,148,149,150,151] techniques. Other works enhance the techniques using game theory optimization algorithms [152,153], while some use heuristic [154,155,156,157,158,159,160] and metaheuristic [161,162,163,164,165] algorithms. ...
Thesis
Full-text available
The rapid deployment of Electric Vehicles (EVs) and usage of renewable energy in day-to-day activities of energy consumers have contributed toward the development of a greener smart community. However, load balancing problems, security threats, privacy leakages, and lack of incentive mechanisms remain unresolved in energy systems. Many approaches have been used in the literature to solve the aforementioned challenges. However, these approaches are not sufficient to obtain satisfactory results because of the resource and time-intensiveness of the primitive cryptographic executions on the network devices. In most cases, energy trading systems manage transactions using a centralized approach. This approach increases the risk of a single point of failure and overall system cost. In this study, a blockchain based Local Energy Market (LEM) model considering Home Energy Management (HEM) system and demurrage mechanism is proposed to tackle the issue of a single point of failure in the energy trading system. It allows both the prosumers and consumers to optimize their energy consumption and minimize electricity costs. This model also allows end-users to shift their load to off-peak hours and to use cheap energy from the LEM. On the other hand, users’ privacy leakages are still not solved in blockchain and can limit its usage in many applications. This research also proposes a blockchain based distributed matching and privacy-preservation model that uses a reputation system for both residential homes and EVs to preserve users’ privacy and efficiently allocate energy. A starvation free energy allocation policy is presented in the model. In addition, a charging forecasting scheme for EVs is introduced that allows users to plan and manage their intermittent EVs’ charging. Partial homomorphic encryption based on a reputation system is used to hide the EVs users’ whereabouts. Identity Based Encryption (ID Based encryption) technique is incorporated in the model to preserve the users’ information privacy in the blockchain. Another bottleneck in the energy trading systems is to perform efficient and privacy-preserving transactions. Therefore, an efficient and secure energy trading model leveraging contract theory, consortium blockchain, and a reputation system is proposed. Firstly, a secure energy trading mechanism based on consortium blockchain is developed. Then, an efficient contract theory based incentive mechanism considering the information asymmetry scenario is introduced. Afterwards, a reputation system is integrated to improve x transaction confirmation latency and block creation. Next, a shortest route and distance algorithm is implemented in order to reduce the traveling distance and energy consumption by the EVs during energy trading. Cheating attacks launched by both buyers and sellers are also issues, which are still not resolved. Thus, a mutual-verifiable fairness mechanism during energy trading based on timed commitment is presented. Proof-of-Energy Reputation Generation (PoERG) and Proof-of Energy Reputation Consumption (PoERC) consensus mechanisms are proposed to solve the high computational cost and huge monetary investment issues created by Proof-of-Work (PoW) and Proof-of-Stake (PoS) existing mechanisms. The mechanisms are developed based on reputation where energy trading transactions are audited, validated, and added into blocks of a blockchain. In order to protect the proposed model from impersonation attacks and minimize malicious validators, a two-stage peer-to-peer secure energy trading model based on blockchain is proposed. The proposed model has two layers: a mutual authentication process layer, and a secure and privacy-preserving energy trading layer. Afterwards, an incentive-punishment algorithm is introduced to motivate energy prosumers to contribute more energy in the proposed model. Next, a dynamic contract theory based supply-demand ratio pricing scheme is proposed. The purpose of the proposed pricing scheme is to solve the issues associated with the existing pricing scheme. Also, to preserve the privacy of the actual energy consumption behavior of the trading participants. Furthermore, storage overhead and delay in communication are challenges that need urgent attention, especially in resource constrained devices for sustainable and efficient transactions. Therefore, a consortium blockchain based vehicular system is proposed in this work for secure communication and optimized data storage in Internet of Vehicles (IoV) network. To secure the proposed system from active and passive attacks, an encryption technique and an authentication mechanism are proposed based on public key encryption scheme and hashing algorithm, i.e., Advanced Encryption Standard-256 and Rivest Shamir Adleman (AES-256+RSA), and Keccak-256. It also protects the model from double spending attack. Moreover, a cache memory technique is introduced to reduce service delay and high resource consumption. In the cache memory, the information of frequently used services is stored, which results in the reduction of service delivery delay. Simulation results show that all of the proposed models perform significantly better as compared to the existing schemes.
Conference Paper
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