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Electricity Forecasting for Efficient Energy Management in Smart Grid (PhD Thesis without Source Codes)

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Smart Grid (SG) is a modernized grid that provides efficient, reliable and economic energy to the consumers. Energy is the most important resource in the world and almost everything relies on it. As smart devices are increasing dramatically with the rapid increase in population, there is a need for an efficient energy distribution mechanism. Furthermore, the forecasting of electricity consumption is supposed to be a major constituent to enhance the performance of the SG. Various learning algorithms have been proposed in the literature for efficient load and price forecasting. However, there exist some issues in the proposed work like increased computational complexity. The sole purpose of the work done in this thesis is to efficiently predict electricity load and price using different techniques with minimum computational complexity. Chapter 1 provides an introduction of various concepts present in the power grids. Afterwards, the unified system model, different sub-problems and the contributions made in the thesis are also presented. Chapter 2 discusses the existing work done by different researchers for performing electricity load and price forecasting. In Chapter 3, Enhanced Logistic Regression (ELR) and Enhanced Recurrent Extreme Learning Machine (ERELM) are proposed for performing short-term load and price forecasting. The former is an enhanced form of Logistic Regression (LR); whereas, the weights and biases of the latter are optimized using Grey Wolf Optimizer (GWO). Classification And Regression Tree (CART), Relief-F and Recursive Feature Elimination (RFE) are used for feature selection and extraction. On the basis of selected features, classification is performed using ELR. Moreover, cross validation is done using Monte Carlo and K-Fold methods. In order to ensure optimal and secure functionality of Micro Grid (MG), Chapter 4 focuses on coordinated energy management of traditional and Renewable Energy Sources (RES). Users and MG with storage capacity are taken into account to perform efficient energy management. A two stage Stackelberg game is formulated. Every player in the game tries to increase its payoff, and ensure user comfort and system reliability. Furthermore, two forecasting techniques are proposed in order to forecast Photo-Voltaic Cell (PVC) generation for announcing optimal prices. Both the existence and uniqueness of Nash Equilibrium (NE) for the energy management algorithm are also considered. In Chapter 5, a novel forecasting model, termed as ELS-net, is proposed. It is a combination of an Ensemble Empirical Mode Decomposition (EEMD) method, multi-model Ensemble Bi Long Short Term Memory (EBiLSTM) forecasting technique and Support Vector Machine (SVM). In the proposed model, EEMD is used to distinguish between linear and non-linear Intrinsic Mode Functions (IMFs). EBiLSTM is used to forecast the non-linear IMFs and SVM is employed to forecast the linear IMFs. The usage of separate forecasting techniques for linear and non-linear IMFs decreases the computational complexity of the model. In Chapter 6, a novel deep learning model, termed as Gated-FCN, is introduced for short-term load forecasting. The key idea is to introduce automated feature selection and a deep learning model for forecasting, which includes an eight layered FCN (FCN-8). It ensures that hand crafted feature selection is avoided as it requires expert domain knowledge. Furthermore, Gated-FCN also helps in reducing noise as it learns internal dependencies as well as the correlation of the time-series. Enhanced Bidirectional Gated Recurrent Unit (EBiGRU) model is dovetailed with FCN-8 in order to learn temporal long-term dependencies of the time-series. Furthermore, weight averaging mechanism of multiple snapshot models is adapted in order to take optimized weights of BiGRU. At the end of FCN-8 and BiGRU, a fully connected dense layer is used that gives final prediction results. The simulations are performed and the results are provided at the end of each chapter. In Chapter 3, the simulations are performed using UMass electric and UCI datasets. ELR shows better performance with the former dataset; whereas, ERELM has better accuracy with the latter. The proposed techniques are then compared with different benchmark schemes. The comparison is done to verify the adaptivity of the proposed techniques. The simulation results show that the proposed techniques outperform the benchmark schemes and increase the prediction accuracy of electricity load and price. Similarly, in Chapter 4, simulations are performed using Elia, Belgium dataset. The results clearly show that the proposed game theoretic approach along with storage capacity optimization and forecasting techniques give benefits to both users and MG. In Chapter 5, simulations are performed to examine the effectiveness of the proposed model using two different datasets: New South Wales (NSW) and Victoria (VIC). From the simulation results, it is obvious that the proposed ELS-net model outperforms the benchmark techniques: EMD-BILSTM-SVM, EMD-PSO-GA-SVR, BiLSTM, MLP and SVM in terms of forecasting accuracy and minimum execution time. Similarly, the simulation results of Chapter 6 depict that Gated-FCN gives maximum forecasting accuracy as compared to the benchmark techniques. For performance evaluation of the proposed work, different performance metrics are used: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Square Error (RMSE). The overall results prove that the work done in this thesis outperforms the existing work in terms of electricity load and price forecasting, and computational complexity.
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Electricity Forecasting for Efficient Energy
Management in Smart Grid
(PhD Thesis without Source Codes)
By
Aqdas Naz
CIIT/FA17-PCS-003/ISB
PhD Thesis
In
Computer Science
COMSATS University Islamabad
Islamabad - Pakistan
Fall, 2021
COMSATS University Islamabad
Electricity Forecasting for Efficient Energy
Management in Smart Grid
A Thesis Presented to
COMSATS University Islamabad, Islamabad
In partial fulfillment
of the requirement for the degree of
PhD (Computer Science)
By
Aqdas Naz
CIIT/FA17-PCS-003/ISB
Fall, 2021
ii
Electricity Forecasting for Efficient Energy
Management in Smart Grid
A Post Graduate Thesis submitted to the Department of Computer Science as partial
fulfillment of the requirements for the award of Degree of PhD (Computer Science).
Name Registration Number
Aqdas Naz CIIT/FA17-PCS-003/ISB
Supervisor
Dr. Nadeem Javaid
Associate Professor, Department of Computer Science
COMSATS University Islamabad (CUI), Islamabad
Co-Supervisor
Dr Sohail Iqbal
Associate Professor, Department of Computing
School of Electrical Engineering and Computer Science (SEECS), National
University of Sciences and Technology (NUST), Islamabad
iii
Certificate of Approval
This is to certify that the research work presented in this thesis, entitled “Electricity Forecast-
ing for Efficient Energy Management in Smart Grid” was conducted by Ms. Aqdas Naz under
the supervision of Dr. Nadeem Javaid. No part of this thesis has been submitted anywhere
else for any other degree. This thesis is submitted to the Department of Computer Science,
COMSATS University Islamabad, Islamabad, in the partial fulfillment of the requirement for
the degree of Doctor of Philosophy in the field of Computer Science.
Aqdas Naz Signature:
Examinations Committee:
..................................................... .....................................................
External Examiner 1: External Examiner 2:
(Designation and Office Address) (Designation and Office Address)
..................................................... .....................................................
Dr. Nadeem Javaid Dr. Majid Iqbal Khan
Supervisor, Head of Department,
Department of Computer Science Department of Computer Science
CUI, Islamabad CUI, Islamabad
..................................................... .....................................................
Dr. Ehsan Ullah Munir Prof. Dr. Zulfiqar Habib
Chairperson, Dean,
Department of Computer Science Faculty of Information Science
CUI and Technology, CUI
iv
Author’s Declaration
I, Aqdas Naz, CIIT/FA17-PCS-003/ISB hereby state that my PhD thesis titled “Electricity
Forecasting for Efficient Energy Management in Smart Grid” is my own work and has not
been submitted previously by me for taking any degree from this University i.e., COMSATS
University Islamabad or anywhere else in the country/world.
At any time if my statement is found to be incorrect even after I graduate the University has
the right to withdraw my PhD degree.
Date: 18-08-2021 Signature:
Aqdas Naz
CIIT/FA17-PCS-003/ISB
v
Plagiarism Undertaking
I solemnly declare that research work presented in this thesis titled, “Electricity Forecasting
for Efficient Energy Management in Smart Grid” is solely my research work with no signifi-
cant contribution from any other person. Small contribution/help wherever taken has been
duly acknowledged and that complete thesis has been written by me.
I understand the zero tolernace policy of HEC and COMSATS University Islamabad towards
plagiarism. Therefore, I as an author of the above titled thesis declare that no portion of my
thesis has been plagiarized and any material used as reference is properly referred/cited.
I undertake if I am found guilty of any formal plagiarism in the above titled thesis even after
award of PhD degree, the University reserves the right to withdraw/revoke my PhD degree
and that HEC and the university has the right to publish my name on the HEC/university
website on which names of students are placed who submitted plagiarized thesis.
Date: 18-09-2021 Signature:
Aqdas Naz
CIIT/FA17-PCS-003/ISB
vi
Certificate
It is certified that Aqdas Naz, CIIT/FA17-PCS-003/ISB has carried out all the work related
to this thesis under my supervision at the Department of Computer Science, COMSATS
University Islamabad, Islamabad and the work fulfills the requirement for award of PhD
degree.
Date: 18-09-2021 Supervisor:
Dr. Nadeem Javaid
Associate Professor
Co-Supervisor:
Dr Sohail Iqbal
Associate Professor
Head of Department:
Dr. Majid Iqbal Khan
Associate Professor
Department of Computer Science
vii
DEDICATION
This research work is dedicated
to
My Beloved Parents,
My Supervisor: "Dr. Nadeem Javaid",
My Husband, Children and Family
viii
ACKNOWLEDGMENTS
I would like to express my sincere gratitude to my advisors Dr. Nadeem Javaid and Dr. Sohail
Iqbal for their support throughout my research work. I would also like to acknowledge the
support of my parents, family and friends.
Aqdas Naz
CIIT/FA17-PCS-003/ISB
ix
ABSTRACT
Electricity Forecasting for Efficient Energy Management in Smart
Grid
Smart Grid (SG) is a modernized grid that provides efficient, reliable and economic energy to
the consumers. Energy is the most important resource in the world and almost everything
relies on it. As smart devices are increasing dramatically with the rapid increase in population,
there is a need for an efficient energy distribution mechanism. Furthermore, the forecasting
of electricity consumption is supposed to be a major constituent to enhance the performance
of the SG. Various learning algorithms have been proposed in the literature for efficient load
and price forecasting. However, there exist some issues in the proposed work like increased
computational complexity. The sole purpose of the work done in this thesis is to efficiently
predict electricity load and price using different techniques with minimum computational
complexity. Chapter 1 provides an introduction of various concepts present in the power
grids. Afterwards, the unified system model, different sub-problems and the contributions
made in the thesis are also presented. Chapter 2 discusses the existing work done by different
researchers for performing electricity load and price forecasting. In Chapter 3, Enhanced
Logistic Regression (ELR) and Enhanced Recurrent Extreme Learning Machine (ERELM)
are proposed for performing short-term load and price forecasting. The former is an enhanced
form of Logistic Regression (LR); whereas, the weights and biases of the latter are optimized
using Grey Wolf Optimizer (GWO). Classification And Regression Tree (CART), Relief-F
and Recursive Feature Elimination (RFE) are used for feature selection and extraction. On the
basis of selected features, classification is performed using ELR. Moreover, cross validation
is done using Monte Carlo and K-Fold methods. In order to ensure optimal and secure
functionality of Micro Grid (MG), Chapter 4 focuses on coordinated energy management of
traditional and Renewable Energy Sources (RES). Users and MG with storage capacity are
taken into account to perform efficient energy management. A two stage Stackelberg game is
formulated. Every player in the game tries to increase its payoff, and ensure user comfort and
system reliability. Furthermore, two forecasting techniques are proposed in order to forecast
Photo-Voltaic Cell (PVC) generation for announcing optimal prices. Both the existence
and uniqueness of Nash Equilibrium (NE) for the energy management algorithm are also
considered. In Chapter 5, a novel forecasting model, termed as ELS-net, is proposed. It is a
x
combination of an Ensemble Empirical Mode Decomposition (EEMD) method, multi-model
Ensemble Bi Long Short Term Memory (EBiLSTM) forecasting technique and Support
Vector Machine (SVM). In the proposed model, EEMD is used to distinguish between linear
and non-linear Intrinsic Mode Functions (IMFs). EBiLSTM is used to forecast the non-linear
IMFs and SVM is employed to forecast the linear IMFs. The usage of separate forecasting
techniques for linear and non-linear IMFs decreases the computational complexity of the
model. In Chapter 6, a novel deep learning model, termed as Gated-FCN, is introduced for
short-term load forecasting. The key idea is to introduce automated feature selection and
a deep learning model for forecasting, which includes an eight layered FCN (FCN-8). It
ensures that hand crafted feature selection is avoided as it requires expert domain knowledge.
Furthermore, Gated-FCN also helps in reducing noise as it learns internal dependencies
as well as the correlation of the time-series. Enhanced Bidirectional Gated Recurrent Unit
(EBiGRU) model is dovetailed with FCN-8 in order to learn temporal long-term dependencies
of the time-series. Furthermore, weight averaging mechanism of multiple snapshot models is
adapted in order to take optimized weights of BiGRU. At the end of FCN-8 and BiGRU, a
fully connected dense layer is used that gives final prediction results. The simulations are
performed and the results are provided at the end of each chapter. In Chapter 3, the simulations
are performed using UMass electric and UCI datasets. ELR shows better performance with
the former dataset; whereas, ERELM has better accuracy with the latter. The proposed
techniques are then compared with different benchmark schemes. The comparison is done
to verify the adaptivity of the proposed techniques. The simulation results show that the
proposed techniques outperform the benchmark schemes and increase the prediction accuracy
of electricity load and price. Similarly, in Chapter 4, simulations are performed using Elia,
Belgium dataset. The results clearly show that the proposed game theoretic approach along
with storage capacity optimization and forecasting techniques give benefits to both users and
MG. In Chapter 5, simulations are performed to examine the effectiveness of the proposed
model using two different datasets: New South Wales (NSW) and Victoria (VIC). From the
simulation results, it is obvious that the proposed ELS-net model outperforms the benchmark
techniques: EMD-BILSTM-SVM, EMD-PSO-GA-SVR, BiLSTM, MLP and SVM in terms
of forecasting accuracy and minimum execution time. Similarly, the simulation results
of Chapter 6 depict that Gated-FCN gives maximum forecasting accuracy as compared to
xi
the benchmark techniques. For performance evaluation of the proposed work, different
performance metrics are used: Mean Absolute Percentage Error (MAPE), Mean Absolute
Error (MAE), Mean Squared Error (MSE) and Root Mean Square Error (RMSE). The overall
results prove that the work done in this thesis outperforms the existing work in terms of
electricity load and price forecasting, and computational complexity.
xii
Journal Publications
4
Aqdas Naz, Nadeem Javaid, Muhammad Asif, Muhammad Umar Javed, Abrar Ahmed,
Sardar Muhammad Gulfam, Muhammad Shafiq and Jin-Ghoo Choi. (2021). Electricity
Consumption Forecasting using Gated-FCN with Ensemble Strategy, IEEE Access,
Volume NN, Pages NN. ISSN: 2169-3536. [IF=3.367]. Download.
3
Nadeem Javaid, Aqdas Naz, Rabiya Khalid, Ahmad Almogren, Muhammad Shafiq and
Adia Khalid. (2020). ELS-net: A New Approach to Forecast Decomposed Intrinsic
Mode Functions of Electricity Load. IEEE Access, 8, 198935-198949. [IF= 3.367].
Download.
2
Aqdas Naz, Nadeem Javaid, Muhammad Babar Rasheed, Abdul Haseeb, Musaed
Alhussein and Khursheed Aurangzeb. (2019). Game Theoretical Energy Manage-
ment with Storage Capacity Optimization and Photo-Voltaic Cell Generated Power
Forecasting in Micro Grid. Sustainability, 1-22. [IF=3.251]. Download.
1
Aqdas Naz, Muhammad Umar Javed, Nadeem Javaid, Tanzila Saba, Musaed Alhussein
and Khursheed Aurangzeb. (2019). Short-Term Electric Load and Price Forecasting
Using Enhanced Extreme Learning Machine Optimization in Smart Grids, Energies,
1-30. [IF=3.004]. Download.
xiii
Conference Proceedings/Book Chapters
8
Aqdas Naz, Nadeem Javaid, Adia Khalid, Muhammad Shoaib, Muhammad Imran.
Electric Load Forecasting using EEMD and Machine Learning Techniques, in the 16th
International Wireless Communications and Mobile Computing Conference (IWCMC),
2020. Download.
7
Aqdas Naz, Nadeem Javaid, Abdul Basit Majeed Khan, Muhammad Mudassar Iqbal,
Muhammad Aqeel ur Rehman Hashmi and Raheel Ahmad Abbasi. Game-Theoretical
energy management for residential user and micro grid for optimum sizing of photo
voltaic battery systems and energy prices, in Workshops of the 33rd International
Conference on Advanced Information Networking and Applications (AINA), 2019.
Download
6
Aqdas Naz, Nadeem Javaid and Sakeena Javaid. Enhanced Recurrent Extreme Learning
Machine using Gray Wolf Optimization for Load Forecasting, in the 21st International
Multitopic Conference (INMIC), 2018. Download.
5
Talha Naeem Qureshi, Nadeem Javaid, Aqdas Naz, Waseem Ahmad, Muhammad Imran
and Zahoor Ali Khan. A Noval Meta-Heuristic Hybrid Enhanced Differential Harmony
Wind Driven (EDHWDO) Optimization Technique For Demand Side Management in
Smart Grid, in 32nd International Conference on Advanced Information Networking
and Applications Workshops (WAINA), 2018. Download.
4
Aqdas Naz, Nadeem Javaid, Talha Naeem Qureshi, Muhammad Imran, Mujahid Ali
and Zahoor Ali Khan. EDHBPSO: Enhanced Differential Harmony Binary Particle
Swarm Optimization, in 32nd International Conference on Advanced Information
Networking and Applications Workshops (WAINA) 2018. Download.
3
Isra Shafi, Nadeem Javaid, Aqdas Naz, Yasir Amir, Israr Ishaq and Kashif Naseem.
Feature Selection and Extraction along with Electricity Price forecasting using Big
Data Analytics, in 12th International Conference on Innovative Mobile and Internet
Services in Ubiquitous Computing (IMIS), 2018. Download.
2
Aqdas Naz, Nadeem Javaid, Muhammad Mudassar Iqbal, Mujahid Ali, Muhammad Im-
ran and Zahoor Ali Khan. TBEENISH: Threshold Balanced Energy Efficient Network
Integrated Super Heterogeneous Protocol for WSNs, in 32nd International Conference
xiv
on Advanced Information Networking and Applications Workshops (WAINA), 2018.
Download.
1
Muqaddas Naz, Nadeem Javaid, Urva Latif, Talha Naeem Qureshi, Aqdas Naz and
Zahoor Ali Khan. Efficient Power Scheduling in Smart Homes using Meta Heuristic
Hybrid Grey Wolf Differential Evolution Optimization Technique, in 32nd International
Conference on Advanced Information Networking and Applications (AINA), 2018.
Download.
xv
TABLE OF CONTENTS
1 Introduction ..................................................................... 1
1.1 Problem Statement ............................................................... 4
1.1.1 Sub-problem 1: Short-term Electric Load and Price Forecast-
ing using Enhanced Extreme Learning Machine Optimization
in Smart Grids ............................................................ 4
1.1.2 Sub-problem 2: Game theoretical Energy Management with
Storage Capacity Optimization and Photo-Voltaic Cell Gener-
ated Power Forecasting in Micro Grids ............................... 4
1.1.3 Sub-problem 3: ELS-net: A New Approach to Forecast Decom-
posed Intrinsic Mode Functions of Electricity Load ................ 5
1.1.4 Sub-problem 4: Electricity Forecasting using Gated-FCN with
Ensemble Strategy ....................................................... 5
1.2 Contributions ...................................................................... 6
1.3 Proposed System Model ......................................................... 7
1.4 Thesis Organization .............................................................. 9
2 Related Work .................................................................... 12
3 Short-term Electric Load and Price Forecasting using Enhanced Ex-
treme Learning Machine Optimization in Smart Grids .................... 26
3.1 Summary of the Chapter......................................................... 27
3.2 Proposed Methodology .......................................................... 27
3.2.1 Classification And Regression Technique ............................ 28
3.2.2 Recursive Feature Elimination ......................................... 28
3.2.3 Relief-F.................................................................... 29
3.2.4 Convolutional Neural Network ........................................ 30
3.2.5 Logistic Regression...................................................... 30
3.2.6 Enhanced Logistic Regression ......................................... 31
3.2.7 Grey Wolf Optimizer .................................................... 31
xvi
3.2.8 Recurrent Extreme Learning Machine ................................ 33
3.2.9 Enhanced Recurrent Extreme Learning Machine ................... 33
3.3 Proposed System Models........................................................ 34
3.3.1 Proposed System Model 1 .............................................. 34
3.3.2 Proposed System Model 2 .............................................. 35
3.4 Simulation Results and Discussion ............................................ 36
3.4.1 Simulation Results and Discussion of Proposed System Model 1 36
3.4.1.1 Data Description .............................................. 36
3.4.1.2 CART........................................................... 37
3.4.1.3 RFE ............................................................. 37
3.4.1.4 Relief-F ........................................................ 38
3.4.1.5 Load Forecasting.............................................. 38
3.4.1.6 Price Forecasting.............................................. 38
3.4.2 Simulation Results and Discussion of Proposed System Model 2 39
3.4.2.1 Data Description .............................................. 39
3.4.2.2 Results’ Discussion........................................... 42
3.5 Performance Metrics ............................................................. 44
3.6 Conclusion of the Chapter....................................................... 50
4 Game Theoretical Energy Management with Storage Capacity Opti-
mization and Photo-Voltaic Cell Generated Power Forecasting in Micro
Grid .............................................................................. 52
4.1 Summary of the Chapter......................................................... 53
4.2 Proposed and Existing Methodology .......................................... 53
4.2.1 Cost Model of Users..................................................... 54
4.2.2 MG Cost Model with Storage Capacity .............................. 56
4.2.3 Game Formulation and Analysis ...................................... 60
4.2.3.1 PVC Power Forecasting Algorithm ........................ 63
4.3 Simulation Results and Discussion ............................................ 66
4.3.1 Dataset description ...................................................... 67
4.3.2 Simulation Results ....................................................... 67
4.4 Conclusion of the Chapter....................................................... 72
xvii
5 ELS-net: A New Approach to Forecast Decomposed Intrinsic Mode
Functions of Electricity Load .................................................. 73
5.1 Summary of the Chapter......................................................... 74
5.2 Problem Formulation ............................................................ 74
5.3 Proposed System Model ......................................................... 75
5.3.0.1 Ensemble Empirical Mode Decomposition ............... 76
5.3.1 The Proposed Multi-model EBiLSTM................................ 78
5.3.1.1 Bi Long Short Term Memory ............................... 78
5.3.1.2 The Proposed Multi-model Ensemble Strategy........... 80
5.3.1.3 Support Vector Machine ..................................... 81
5.4 Simulation Results and Discussion ............................................ 81
5.4.1 Data Description ......................................................... 81
5.4.2 Performance Evaluators................................................. 82
5.4.3 Predictive Analytics of Electricity Consumption Data ............. 82
5.4.4 Computational Cost ..................................................... 90
5.4.5 Scalability Analysis ..................................................... 90
5.5 Conclusion of the Chapter....................................................... 91
6 Electricity Forecasting using Gated-FCN with Ensemble Strategy ....... 93
6.1 Summary of the Chapter......................................................... 94
6.2 Proposed Deep Learning Model................................................ 94
6.2.1 Fully Convolutional Network-8........................................ 95
6.2.1.1 Convolution Layer ............................................ 95
6.2.1.2 Pooling Layer ................................................. 97
6.2.2 Enhanced Bidirectional Gated Recurrent Unit ....................... 97
6.2.3 Cascaded FCN-8 and BiGRU .......................................... 99
6.3 Simulation results and discussion ..............................................101
6.3.1 Data Description .........................................................102
6.3.2 Validation Setting of the Proposed System Model ..................103
6.3.3 Exploratory Data Analysis..............................................103
6.3.4 Predictive Data Analysis ................................................104
xviii
I title 108
6.4 Conclusion of the Chapter.......................................................115
7 Conclusion and Future Work ..................................................119
7.1 Conclusions .......................................................................120
7.2 Future Recommendations .......................................................122
xix
LIST OF FIGURES
1.1 Unified system model .............................................................. 8
1.2 Steps taken during load and price forecasting .................................... 9
3.1 Grey wolf social hierarchy......................................................... 32
3.2 Functioning of RELM ............................................................. 33
3.3 Proposed system model 1 .......................................................... 35
3.4 Proposed system model 2 .......................................................... 35
3.5 One day load prediction using UMass dataset .................................... 39
3.6 One day load prediction using UCI dataset ....................................... 39
3.7 One week load prediction using UMass dataset .................................. 40
3.8 One week load prediction using UCI dataset ..................................... 40
3.9 One month load prediction using UMass dataset ................................. 40
3.10 One month load prediction using UCI dataset .................................... 41
3.11 One day price prediction using UMass dataset ................................... 41
3.12 One week price prediction using UMass dataset ................................. 41
3.13 One month price prediction using UMass dataset ................................ 42
3.14 Daily load consumption taken from 10 different smart meters .................. 44
3.15 ELM regression line plot for UMass dataset...................................... 46
3.16 ELM regression line plot for UCI dataset......................................... 46
3.17 RELM regression line plot for UMass dataset .................................... 46
3.18 RELM regression line plot for UCI dataset ....................................... 47
3.19 ERELM regression line plot for UMass dataset .................................. 47
3.20 ERELM regression line plot for UCI dataset ..................................... 47
4.1 Interaction between user and MG ................................................. 54
4.2 Flow diagram of CS algorithm .................................................... 65
4.3 Flow diagram of GWO algorithm ................................................. 66
4.4 PVC power generation in a certain day ........................................... 67
4.5 Hourly PVC generation ............................................................ 68
xx
4.6 Daily PVC generation.............................................................. 69
4.7 Consumption of users in 24 hours................................................. 69
4.8 Energy distribution without game theory ......................................... 69
4.9 Energy distribution with game theory ............................................. 70
4.10 PVC Energy distribution without game theory ................................... 70
4.11 PVC power generation with game theory ......................................... 71
4.12 MAPE of five different models with PVC power forecasting steps ............. 71
5.1 Input and output of deep learning model.......................................... 75
5.2 Proposed system model ............................................................ 76
5.3 Flow diagram of EBiLSTM ....................................................... 79
5.4 Ensemble strategy in BiLSTM .................................................... 80
5.5 Original NSW dataset .............................................................. 82
5.6 Original VIC dataset ............................................................... 82
5.7 Extracted IMFs - NSW dataset .................................................... 83
5.8 Extracted IMFS - VIC dataset ..................................................... 83
5.9 RMSE for NSW .................................................................... 84
5.10 MAE for NSW ..................................................................... 84
5.11 MAPE for NSW .................................................................... 84
5.12 Prediction results of electricity consumption for Summer season using NSW.. 85
5.13 Prediction results of electricity consumption for Autumn season using NSW .. 85
5.14 Prediction results of electricity consumption for Winter season using NSW . . . 85
5.15 Prediction results of electricity consumption for Spring season using NSW.. .. 86
5.16 RMSE for VIC ..................................................................... 86
5.17 MAE for VIC....................................................................... 87
5.18 MAPE for VIC ..................................................................... 87
5.19 Prediction results of electricity consumption for Summer season using VIC ... 87
5.20 Prediction results of electricity consumption for Winter season using VIC. ... . 88
5.21 Prediction results of electricity consumption for Spring season using VIC ... .. 88
5.22 Prediction results of electricity consumption for Autumn season using VIC .. . 88
6.1 Gated-FCN architecture............................................................ 95
6.2 Activation functions ............................................................... 96
xxi
6.3 Max pooling and average pooling ................................................. 98
6.4 BiGRU architecture ................................................................ 99
6.5
Ensemble strategy illustration - The number of snapshot models are selected
on the basis of minimum loss...................................................... 99
6.6 2011-2019 ISO-NE hourly load consumption ....................................104
6.7 Weekend and Weekday load consumption ........................................104
6.8 Load forecasting using MAPE ....................................................106
6.9 Daily load consumption forecasting...............................................109
6.10 Weekly load consumption forecasting ............................................110
6.11 Load consumption - Monday ......................................................110
6.12 Load consumption - Tuesday ......................................................110
6.13 Load consumption - Wednesday................................................... 111
6.14 Load consumption - Thursday .....................................................111
6.15 Load consumption - Friday ........................................................111
6.16 Load consumption - Saturday .....................................................112
6.17 Load consumption - Sunday .......................................................112
6.18 Loss of CNN-LSTM ...............................................................112
6.19 Loss of Gated-FCN ................................................................113
xxii
LIST OF TABLES
1.1 Comparison of different levels of the proposed system model................... 10
2.1 Summary of related work .......................................................... 18
3.1 Features in UMass electric dataset ................................................ 37
3.2 Results of CART for UMass electric dataset ..................................... 37
3.3 RFE features for UMass electric dataset .......................................... 38
3.4 Relief-F features for UMass electric dataset ...................................... 38
3.5 Obtained RMSE using ELM, RELM and ERELM............................... 44
3.6
Obtained RMSE for half-yearly testing data using ELM, RELM and ERELM
by Monte Carlo and K-Fold cross validation...................................... 45
3.7
Obtained RMSE for yearly testing data using ELM, RELM and ERELM by
Monte Carlo and K-Fold cross validation. ........................................ 45
3.8 Computational time comparison of ERELM, RELM and ELM execution. .. .. . 45
3.9
Load performance metrics comparison for one day using the UMass electric
dataset............................................................................... 48
3.10
Load performance metrics comparison for one week using the UMass electric
dataset............................................................................... 48
3.11
Load performance metrics comparison for one month using the UMass elec-
tric dataset .......................................................................... 48
3.12
Price performance metrics comparison for one day using the UMass electric
dataset............................................................................... 49
3.13
Price performance metrics comparison for one week using the UMass electric
dataset............................................................................... 49
3.14
Price performance metrics comparison for one month using the UMass elec-
tric dataset .......................................................................... 49
3.15 Load performance metrics comparison for one day using the UCI dataset . .. .. 49
3.16 Load performance metrics comparison for one week using the UCI dataset. .. . 50
3.17 Load performance metrics comparison for one month using the UCI dataset .. 50
xxiii
3.18 Accuracy of ERELM using RMSE, MSE and MAE for half-yearly data ....... 50
3.19 Accuracy of ERELM using RMSE, MSE and MAE for yearly data ............ 51
4.1 Pricing model parameters.......................................................... 68
4.2 PVC generation storage battery parameters....................................... 68
5.1 Performance results ................................................................ 89
5.2
Obtained MAPE for NSW and VIC testing datasets using multistep against
ELS-net, EMD-BiLSTM-SVM and BiLSTM by K-Fold cross validation. ..... 89
5.3 Computational time (sec) ......................................................... 90
6.1 Validation setting of FCN model ..................................................103
6.2 Validation setting of BiGRU model ..............................................103
6.3 Exploring activation functions.....................................................105
6.4 Performance results - Monday.....................................................114
6.5 Performance results - Tuesday.....................................................114
6.6 Performance results - Wednesday .................................................115
6.7 Performance results - Thursday ...................................................115
6.8 Performance results - Friday.......................................................116
6.9 Performance results - Saturday ....................................................116
6.10 Performance results - Sunday......................................................117
6.11
Comparison of Gated-FCN with existing models using noise added consumption
117
6.12 Computational time (sec) ..........................................................118
6.13 Accuracy of Forecasting Techniques ..............................................118
xxiv
LIST OF ABBREVIATIONS
ACF Auto Correlation Function
ADF Augmented Dickey Fuller
AEMO Australian Electricity Market Operator
AI Artificial Intelligence
AIC Akaike Information Criteria
ANN Artificial Neural Network
AR Auto regressive
ARIMA Auto Regressive Integrated Moving Average
ARMAX Auto Regressive Moving Average with Exogenous variables
BIC Bayesian Information Criteria
BiGRU Bidirectional Gated Recurrent Unit
BiLSTM Bidirectional Long Short Term Memory
BP Back Propagation
CARIMA Cuckoo Search Optimized ARIMA
CART Classification And Regression Technique
CNN Convolutional Neural Network
CS Cuckoo Search
DAE Deep Auto Encoders
DBN Deep Belief Net
DEM Distributed Energy Management
DE-SVM Differential Evolution Support Vector Machine
DE-ELM Differential Evolution Extreme Learning Machine
DLSTM Dilated LSTM
DNN Deep Neural Network
DR Demand Response
DRN Deep Residual Network
DSM Demand Side Management
DT Decision Tree
DWT Discrete Wavelet Transform
EBiLSTM Ensemble Bi Long Short Term Memory
xxv
EEMD Ensemble Empirical Mode Decomposition
EEMD-DBN Ensemble Empirical Mode Decomposition Deep Belief Network
EPEX European Power Exchange
ELR Enhanced Logistic Regression
ELM Extreme Learning Machine
ELS-net EEMD BiLSTM SVM Network
EMD Empirical Mode Decomposition
ERELM Enhanced Recurrent Extreme Learning Machine
ES Exponential Smoothing
ESAE Enhanced Stacked Auto Encoder
EUNITE EUropean Network of excellence on Intelligent TEchnologies for smart
adaptive systems
FCN-8 Fully Convolutional Network
FFNN Feed Forward Neural Network
Gated FCN Gated Fully Convolutional Network
GA Genetic Algorithm
GANN Generalized Artifical Neural Network
GARIMA Grey Wolf Optimized ARIMA
GASVM Genetic Algorithm Support Vector Machine
GCA Grey Correlation Analysis
GRU Gated Recurrent Unit
GSAE Genetic Stacked Auto encoder
GWO Grey Wolf Optimization
HHT Hilbert-Huang Transform
ICT Information and Communication Technology
IMF Intrinsic Mode Function
ISONE Independent System Operators New England
ISO NECA Independent System Operator New England Control Area
KELM Kernel Extreme Learning Machine
KPCA Kernel Principal Component Analysis
LR Logistic Regression
xxvi
LSTM Long Short Term Memory
MAE Mean Absolute Error
MAPE Mean Absolute Percentage Error
MG Micro Grid
MISO Midcontinent Independent System Operator
MLP Multi Layer Perceptron
MLR Multi Linear Regression
MSE Mean Square Error
NLS-SVM Nonlinear Least Square Support Vector Machine
NE Nash Equilibrium
NN Neural Network
NSW New South Wales
NYISO New York Independent System Operator
OS-ELM Online Sequential Extreme Learning Machine
PACF Partial Auto Correlation Function
PAR Peak Average Ratio
PJM Pennsylvania–New Jersey–Maryland Interconnection
PSO Particle Swarm Optimization
PVC Photo-Voltaic Cell
QLD Queensland
RBM Restricted Boltzmann Machine
RELM Recurrent Extreme Learning Machne
ReLU Rectified Linear Unit
RES Renewable Energy Sources
RFE Recursive Feature Elimination
RMSE Root Mean Square Error
RNN Recurrent Neural Network
RTP Real Time Pricing
SARIMA Seasonal Auto Regressive Integrated Moving Average
SBELM Sparse Bayesian Extreme Learning Machine
SDA Stacked De-noising Autoencoders
xxvii
SG Smart Grid
SLFN Single Layer Feedforward Network
SM Smart Meters
SMI Smart Meter Infrastructure
SVM Support Vector Machine
SVR Support Vector Regression
TG Traditional Grid
ToU Time of Use
TVC-ABC Time Varying Coefficients Artificial Bee Colony
VIC Victoria
xxviii
NOMENCLATURE
AvActual value
AvActual time-series
ak,bkFixed parameters
bl(k)Total energy required to discharge battery
CkxkCost function of user
Cbat
m(y)Daily depreciation cost function
Csp
nTotal cost of solar power generation
Cn,kTotal cost of Nusers
enEnergy demanded from users
eb
m(k)Energy required to charge battery
FPenalty factor
FvPredicted time-series
fmList of features
ftForget gate
h Step size
hk
ch Pattern of battery charging
hk
disch Pattern of battery discharging
htHidden layer
itInput gate
KTime slots in a period
lnDaily Energy usage of user
m Mean
MNumber of ensembles
mNumber of snapshot models
N Total number of samples
NMultiple residential users
nNumber of hidden nodes
nt(i)White noise
otOutput gate
PkReal time price of each slot
xxix
P
vPredicted time-series
QMicro Grid
QTest static
ri,sFinal residue
sState of battery
TTotal number of observations
T Total time duration
t Time slot
UnProfit of MG
Xaverage Average user consumption
XkSum of total energy
Xpeak Peak consumption
x Actual value
xn(k)Energy demand in a particular slot
x’ Predicted value
y Output
ym
lLower limit of battery capacity
yu
mUpper limit of battery capacity
αFittest wolf 1
βFittest wolf 2
δFittest wolf 3
ωRemaining wolves
ηch Charging efficiency of a battery
ηdisch Discharging efficiency of a battery
σ2
εConstant variance
UCost function of users
BCost function of a MG
εtError at time t
σ2Mean square error
δPrediction error
ςStrategy form
xxx
λsSolar power selling price
φiVector based on rules of AR
θiVector based on rules of MA
xxxi
Chapter 1
Introduction
1
Conventionally, Traditional Grids (TGs) are being used for electricity generation and distribu-
tion. As the time passes, the infrastructure of TG is getting obsolete, which results in energy
loss and less efficient output. Due to the usage of outdated infrastructure, intensive power loss
is being faced, which further leads to various issues like load shedding, blackouts, voltage
fluctuations, etc., [
1
]-[
5
]. TGs use fossil fuels like coal, petrol, diesel, etc., for the combustion
process of turbines. The extensive burning of fossil fuels leads to natural resource depletion
and increase in pollution. To tackle the aforementioned issues, authors and researchers have
suggested to use Renewable Energy Sources (RES) and modify the existing TGs by incorpo-
rating the latest technologies and modern infrastructure. The new and modified form of TG is
the Smart Grid (SG) [
6
]-[
12
], which involves bi-directional communication between users
and the utility [
13
]-[
17
]. It also includes forecasting of electricity load and price. The accurate
and efficient forecasting helps the power companies to perform enhanced grid construction
planning. It assists in the improvement of economic and social benefits of the power systems.
Moreover, forecasting is also done to schedule the load consumption from on-peak hours to
off-peak hours for the next day, week or month to reduce the electricity cost and enhance the
user comfort [
18
]-[
25
]. Furthermore, the accurately forecasted data is also very effective in
predicting the potential faults of the system. Thus, security of the power grids can be ensured
using predicted data. Load forecasting also helps to ensure the balance between supply and
demand of electricity. Consequently, energy generation can be managed without wasting or
generating surplus energy.
A detailed analysis is performed on the time-series data, which aims to extract meaningful
statistics and other important features from it [
26
]. The time-series data is captured and
analyzed in two main forms: uni-variate and multi-variate [
27
]. Time-series data has cyclical
or seasonal characteristics that are influential in the context of its analysis. In order to perform
analysis on cyclic characteristics, different models have been proposed in the literature, e.g., a
pattern recognition framework is proposed to locate the changes in cyclic time-series, which
combines linear discriminant as well as K-means clustering method [
28
]. In time-series load
forecasting paradigm, there exist four types of forecasting horizons: long-term, medium-term,
short-term and very short-term [
29
]. Forecasting of electricity consumption is a challenging
task. Climate changes and social activities are considered as external factors that highly affect
the linearity and predictability of the data [
30
]. Several statistical linear methods are used for
time-series forecasting. The goal of these methods is to extrapolate the energy requirements
of future. Besides, the most successful techniques are based on Holt winters exponential
smoothing [
31
] as well as Auto Regressive Integrated Moving Average (ARIMA) [
32
]-[
35
].
2
In the past few years, rapid development in the field of computational intelligence, which in-
cludes Artificial Neural Network (ANN) [
36
]-[
39
], Support Vector Machine (SVM) [
40
]-[
43
]
and fuzzy comprehensive evaluation [
44
], has been observed. ANN is extensively used in
literature for regression and classification purposes. However, it has the limitation that it often
gets trapped in local optima
[45]
. Apart from the convergence issue, short-term load forecast-
ing is also a challenge due to its non-stationary load and long-term dependency forecasting
horizon [
46
]-[
50
]. Thus, deep forecasting techniques are given due importance these days
[
51
]. These techniques use multiple stacked hidden layers to achieve the level of abstraction
and give better learning ability. Moreover, the extension of Recurrent Neural Network (RNN),
i.e., Long Short Term Memory (LSTM), is gaining attention in the field of machine learning
due to its dependency learning ability and memory storage [
52
]-[
56
]. However, LSTM only
learns the past dependencies in the training data. On the other hand, Bidirectional LSTM
(BiLSTM) learns both past and future dependencies. Deep learning model varies from data
to data; therefore, it is required to increase the generalization capabilities of the model.
Ensemble learning methods are proven to have better forecasting capabilities. These methods
strategically combine multiple forecasting algorithms and perform regression and classifica-
tion on uni-variate and multi-variate data. There are three basic reasons behind the success
of ensemble methods: statistical, computational and representational [
57
]. Many different
ensemble methods are in practice nowadays like ensemble of Deep learning Belief Networks
(DBNs) and ensemble of Extreme Learning Machines (ELMs) [
58
,
59
]. Among different
ensemble methods, divide and conquer rule performs the best using time-series data [
60
].
Forecasting techniques are also combined with the time-series decomposition techniques in
the literature. Empirical Mode Decomposition (EMD) is one of the decomposition methods
that is used for time-series decomposition. It is a part of the Hilbert-Huang Transform (HHT)
method. With the latest technological advancements made in the power sector, efficient
load forecasting has become a necessity. Keeping this in mind, different electricity load
forecasting methods have been developed over the past few years. Starting from conventional
neural network techniques like ANN, CNN, etc.,
[61]-[66]
, the focus has been shifted to-
wards hybrid and other meta heuristic techniques like CNN-LSTM, EMD, etc.,
[67]-[72]
.
Conventional techniques perform well up to a certain limit. However, these techniques have
some major limitations like overfitting, premature convergence, mode mixing, etc. Due to
these limitations, no technique is able to completely solve the trade-off between forecasting
accuracy and computational complexity. Therefore, the work provided in the thesis is carried
out to accurately forecast electricity consumption and proceed with minimum computational
3
complexity. Simulation results given at the end of each chapter prove the efficacy of the work
done in the thesis.
1.1 Problem Statement
This section provides the problem statement of the thesis in the form of multiple sub-problems.
1.1.1 Sub-problem 1: Short-term Electric Load and Price Forecasting using
Enhanced Extreme Learning Machine Optimization in Smart Grids
The data of SGs is increasing drastically with the increase in population, so an efficient tech-
nique is required to predict electricity load and price [
73
]-[
77
]. Authors in [
78
] use Recurrent
Extreme Learning Machine (RELM) to predict the electricity load. However, in RELM,
weights and biases are randomly assigned, which leads to drastic variations in prediction
results. An enhanced technique is proposed to solve the aforesaid issue. In [
79
], authors use
Convolutional Neural Network (CNN) for predicting the energy demand. However, CNN
involves tuning of a number of layers, which makes it spatio-temporal complex. In this thesis,
two enhanced techniques are proposed to increase the forecasting accuracy of electricity load
and price. Uni-variate and multi-variate datasets are used for both techniques. Furthermore,
analysis of both residential and utility data is performed collectively.
1.1.2 Sub-problem 2: Game theoretical Energy Management with Storage Ca-
pacity Optimization and Photo-Voltaic Cell Generated Power Forecast-
ing in Micro Grids
To make efficient use of renewable resources, different techniques are proposed in the past.
However, Distributed Energy Management (DEM) problem is not tackled well. Moreover, the
authors try to achieve the objective function without simultaneously dealing with user comfort
and utility profit. On the contrary, the work done in this thesis aims to maximize the objective
function of each player while satisfying users’ demand of electricity and guaranteeing reliable
energy system operations. Due to uncontrollable and uncertain nature of energy generation
by RES [
80
]-[
89
], different techniques are used for short-term electricity load forecasting.
However, the conventional techniques do not perform efficient load forecasting. In addition,
the problem of energy management in a distributed Micro Grid (MG) also needs to be tackled
[
90
]-[
94
]. The problem is addressed in the proposed work using non-cooperative game theory
as it ignores common commitment of players and has minimum communication overhead
4
[
95
]. The optimization of storage capacity in MG plays a vital role in context of efficient
management of electric load [96], which is also addressed in this thesis.
1.1.3 Sub-problem 3: ELS-net: A New Approach to Forecast Decomposed In-
trinsic Mode Functions of Electricity Load
With the advancements made in the technological sector and the drastic increase in population
over the past few decades, the electricity demand has also increased tremendously. However,
electricity generation has not increased with the same ratio, which leads to an imbalance
between electricity demand and supply. This imbalance further leads to many other issues like
load shedding, blackouts, increased electricity price, burden on power grids, etc. Electricity
theft detection is also one of the major issues, which occurs due to expensive electricity
prices [
104
]-[
108
]. To mitigate these issues, accurate load forecasting and Demand Side
Management (DSM) have become the need of the hour [
109
]-[
118
]. Keeping this in view,
researchers have proposed various methods for accurately forecasting electricity demand
like ANN
[99]
, LSTM
[119]
, combination of Ensemble Empirical Mode Decomposition
(EEMD) and Support Vector Regression (SVR)
[120]
, etc. ANN suffers from overfitting
and premature convergence problems. It does not perform well for time-series data. The
authors in
[119]
use a variant of RNN, i.e., LSTM, to forecast electricity consumption using
time-series data. Observation of the electricity demand at equal time intervals is termed as
electricity data, also known as the time-series data. There exist two types of time-series
data: linear and non-linear. Using same forecasting method for both types of data series
increases the computational complexity. The authors in
[120]
use EEMD-SVR to decompose
time-series into sub-series. SVR is used to forecast electricity demand. However, it does not
perform well with non-linear data. To overcome the aforementioned limitations, a hybrid
model is proposed in the underlying work with emphasis on denoising of time-series data.
Moreover, the electricity demand time-series data is decomposed into two sub-series: linear
and non-linear. The non-linear data is forecasted using Ensemble Bi Long Short Term
Memory (EBiLSTM); whereas, the linear data is forecasted using SVM.
1.1.4 Sub-problem 4: Electricity Forecasting using Gated-FCN with Ensemble
Strategy
Many variants of Deep Artificial Neural Network (DANN) have been proposed in literature
for electricity load forecasting [
122
]-[
125
], such as Restricted Boltzmann Machine (RBM)
[
121
] and ANN with multiple layers [
126
]. However, an increase in the number of layers
5
increases the complexity of the network in terms of execution time and memory requirement.
Furthermore, forecasting of residential electricity load is performed using ANN in [
127
]
where the effectiveness of ANN increases as per the availability of exogenous variables such
as weather data, temperature, wind speed and humidity. The non-availability of dependent
variables adversely affects the proposed model’s performance. Besides, long-term dependen-
cies are hard to learn by traditional RNN as it faces vanishing gradient issue due to time depth
and single hidden layer. To tackle this issue, DANN is used in [
128
], which uses variables’
data to detect latent features. The extracted latent features are fed to Multi Layer Perceptron
(MLP) for forecasting the time-series data. However, MLP performance is very poor for
detecting temporal dependencies. To deal with temporal dependencies, LSTM is used. In
[
129
], authors use LSTM as it is efficient in learning temporal trends in time-series data.
However, the correlation of exogenous and dependent variables plays a vital role in learning
the time-series data, which is not considered in the proposed work. Moreover, LSTM also
requires excessive memory during its execution.
1.2 Contributions
This section provides the contributions made in this thesis.
Data pre-processing is performed after acquiring data from different datasets like
UMass Electric, UCI, Elia, VIC, NSW, ISO-NE, etc. Data pre-processing includes
different steps like data normalization, outliers removal, linear interpolation, etc.
After data pre-processing, feature engineering is performed using different techniques
like Recursive Feature Elimination (RFE), Classification And Regression Technique
(CART), Relief-F, Fully Convolutional Network (FCN), etc. Also, the hand crafted
feature extraction process is replaced with an automated deep learning model, which
automatically selects the features.
Once, the pre-dominant and less redundant features are obtained, electricity load and
price forecasting is performed using the newly proposed techniques like Enhanced
Logistic Regression (ELR), Enhanced Recurrent Extreme Learning Machine (ERELM),
GARIMA, CARIMA, etc. The electricity forecasting is performed both at the SG and
the MG levels.
To increase the forecasting accuracy, game theory is applied in the form of a two
stage Stackelberg game, which ensures Nash Equilibrium (NE). Moreover, for reducing
6
computational complexity, the time-series data is decomposed into linear and non-linear
sub-series, which are forecasted individually.
Simulations are performed to prove the efficacy of the solutions proposed in this thesis
and different performance metrics are used for evaluation. The results clearly show
that the work done in this thesis has the tendency to outperform similar existing works.
1.3 Proposed System Model
In this section, the unified system model of the thesis is presented along with the comparison
of the solutions proposed for the problems identified in Section 1.1. Figure 1.1 shows
the unified system model of the thesis. Whereas, Figure 1.2 shows the major steps taken
while performing load and price forecasting: data acquisition, data pre-processing, feature
engineering (feature selection and extraction) and classification (forecasting). Moreover,
Table 1.1 gives the comparison of the solutions proposed for sub-problems 1.1.11.1.4 and
provided in Chapters 36. Each solution is considered as an individual level of the unified
proposed system model and labeled as L.1–L.4. Level 1 deals with the short-term load
and price forecasting. At this level, the electricity is generated using fossil fuels at the
SG side. For feature engineering, three different methods are used: RFE, Relief-F and
CART. After feature engineering, forecasting is done through two newly proposed techniques:
ELR and ERELM. Both the techniques perform efficiently and outperform the benchmark
techniques: LR, ELM and RELM. However, electricity generation at the SG side causes
increase in carbon emission, electricity cost and power losses. To tackle the issues, the
focus is shifted towards the MG, where electricity is generated using RES. Level 2 of the
proposed model deals with the forecasting of power generated through PVC and its storage
optimization. Efficient energy management is also performed at this level. A Stackelberg
game is formulated between the users and the MG, which ensures NE. Two techniques are
proposed to perform forecasting: GARIMA and CARIMA. No doubt, the issues faced in the
SGs are tackled at this level. Still, forecasting load and price using continuous time-series
data is a time-consuming task. Therefore, there is a need to devise such a mechanism that
will deal with this issue. At level 3 of the proposed system model, the time-series data is
decomposed into linear and non-linear sub-series using EEMD, which helps in forecasting
the electricity load in minimum time. Electricity load forecasting using linear sub-series is
performed using SVM; whereas, EBiLSTM is used for forecasting non-linear time-series
data. The splitting of a bigger time-series data into smaller sub-series forecasts electricity
7
load in a minimum time. However, there is still a scope of improvement if manual feature
extraction is replaced with automated feature extraction. At level 4, Enhanced Bidirectional
Gated Recurrent Unit (EBiGRU) is used in combination with FCN to learn the temporal
dependencies and automatically extract the important features along with the noise removal.
Gated FCN is used for electricity load forecasting. Overall, the work done in this thesis
ensures automated and efficient load and price forecasting at a reduced computational cost
and time.
From critical analysis point of view, it is inferred that the feature engineering performed at
L. 1
L. 3
L. 2
L. 4
Short-term load
and price
forecasting
Storage
optimization
PVC power forecasting
Energy
management
Forecasting of decomposed intrinsic mode functions
Automated short-term load forecasting
Figure 1.1: Unified system model
level 1 is quite beneficial for extracting the important and less redundant features. Moreover,
at level 1, cross-validation is also performed, which proves beneficial in validating the results.
The performance analysis of the model using four different performance metrics also adds to
the efficiency of the model. However, using different techniques for feature engineering, cross
validation and performance analysis leads to an increase in the overall computational time of
the system, which can turn out to be a major issue. The work done at level 2 does not involve
the usage of complex techniques at different steps like feature engineering and also uses less
number of performance metrics. Moreover, simultaneously achieving multiple objectives
increases the computational time and cost. Furthermore, the comparison of work done in
the thesis with multiple state-of-the-art work shows that the proposed work is efficient and
outperforms the existing works. From the scalability point of view, the work done in the thesis
8
Figure 1.2: Steps taken during load and price forecasting
is highly scalable as multiple datasets are used having different time duration. Moreover, the
use of an automated 8-layered deep learning model at level 4 proves the efficacy of the work
proposed in the thesis.
1.4 Thesis Organization
The organization of the thesis is provided in this section. Chapter 2provides the state-
of-the-art related work. Different works done on load and price forecasting are discussed
in the same chapter. Chapter 3presents the proposed model for short-term electric load
and price forecasting in the SGs using enhanced ELM optimization. Two different sets
of techniques are used for load and price forecasting using two different datasets. The
9
Table 1.1: Comparison of different levels of the proposed system model
Characteristics L.1 L.2 L.3 L.4
Main objective(s)
Short-term electric-
ity load and price
forecasting
Energy management,
storage capacity
optimization and
PVC generated
power forecasting
Forecast decom-
posed mode func-
tions
Automated short-
term load forecasting
Technique(s) used ELR and ERELM
Stackleberg game
theory, GARIMA
and CARIMA
EEMD, SVM and
EBiLSTM
EBiGRU and FCN-8
Optimizer(s) used GWO GWO and CS ADAM
Feature engineering
technique(s) used
RFE, CART and
Relief-F
– – FCN
Cross validation tech-
nique(s) used
Monte-Carlo and K-
Fold
K-Fold –
Dataset(s) used UCI and UMass Elia NSW and VIC ISO-NE
Performance metric(s)
used
MAE, MSE, MAPE
and RMSE
MAPE and RMSE
MAE, MAPE and
RMSE
RMSE and MAPE
Benchmark scheme(s)
CNN, LR, ELM and
RELM
SVM, SAE and BP
EMD-BiLSTM-
SVM, EMD-PSO-
GA-SVR, BiLSTM,
SVM and MLP
BiGRU, CNN-
LSTM, MLP and
LSTM
10
existing techniques, simulation results and their discussion are also presented in this chapter.
Moving ahead, Chapter 4discusses the work done on energy management using the game
theoretical approach along with the storage capacity optimization and the forecasting of the
energy generated through PVC. The simulation results and their discussion along with the
comparison between the proposed techniques using game theory and without using it are also
provided. Chapter 5highlights the contributions made and the work done to decompose the
electricity consumption time-series data into linear and non-linear mode functions, and to
forecast both time-series individually. The hybrid model is proposed to perform the required
tasks and the simulation results prove the efficacy of the proposed model. Furthermore,
Chapter 6presents the work done on electricity load forecasting using a hybrid model along
with an ensemble technique. The simulation results and their discussion prove that the work
done in this chapter outperforms similar existing works. Chapter 7concludes the thesis and
presents the future directions.
11
Chapter 2
Related Work
12
Many forecasting techniques have been used in the past for load and price forecasting. These
techniques can be categorized in three main groups: data driven [
130
]-[
134
], classical and
Artificial Intelligence (AI) [
135
]-[
139
]. Data driven techniques consider past data to predict
the desired outcomes. Classical methods comprise of the statistical and mathematical meth-
ods like ARIMA [
140
]-[
144
], Seasonal ARIMA (SARIMA) [
145
]-[
151
], Random Forest
(RF) [
152
]-[
158
], etc. Whereas, AI methods mimic the behaviour of biological neurons like
Feed Forward Neural Network (FFNN), CNN, LSTM, etc. In [
159
], behavioral analytics
are performed using Bayesian network and MLP. A number of experiments are performed
using the data obtained from the smart meters. Both short-term and long-term forecasting
is performed. In [
160
], Multiple Linear Regression (MLR) is used for forecasting purpose.
However, it has the limitation that it can not be used for long-term prediction. The authors
in [
161
] use residual network for forecasting load on the basis of weather data. The au-
thors in [
162
] use RBM to train the data and Rectified Linear Unit (ReLU) to predict the
electricity load. In [
163
], Discrete Wavelet Transform (DWT) and Inconsistency Rate (IR)
methods are proposed to select the optimal features from the feature set, which helps in
dimensionality reduction. Sperm Whale Algorithm (SWA) helps to optimize the parameters
of SVM. The authors in [
164
] propose a model for Short Term Load Forecasting (STLF).
Mutual Information (MI) is used for feature selection; whereas, better forecasting results are
achieved by modifying the ANN. In [
165
], the authors predict 24 hour ahead cooling load
of buildings using deep learning. The results show that deep learning techniques enhance
the load prediction. Similarly in [
166
], the authors use RNN, which groups the consumers
into pool of inputs. The proposed model is implemented using Tensorflow package and it
achieved better results as compared to conventional works.
ELM is a generalized single hidden layer FFNN that is proposed by the authors in [
167
,
168
].
It is proved to be effective in both regression and classification methods. In [
169
]-[
174
], the
authors use the NNs for achieving better load prediction. In ELM learning processes, input
weights and biases are randomly assigned; whereas, the output weights are calculated using
the Moore–Penrose generalized inverse technique. In [
175
], the authors use Sparse Bayesian
ELM for multi-classification purposes. The authors in [
176
] use Particle Swarm Optimiza-
tion (PSO) and Discrete Particle Swarm Optimization (DPSO) techniques for efficient load
forecasting. The authors in [
177
] implement GWO with NNs to optimize weights and biases.
It is prove that optimization of weights and biases increases the efficiency of the network.
In [
178
], ELM is trained using Back Propagation (BP) by using context neurons as input to
hidden and input layers. Accuracy is improved by further adjusting weights using previous
13
iteration errors; whereas, biases and neurons selection affect prediction accuracy as already
discussed in [78].
In [
179
], different deep learning models are used for price forecasting. Based on the simula-
tion results, it is proved that deep learning models perform better than the statistical models.
In this paper, GRU is used, which is a variant of RNN. GRU outperform LSTM and many
other statistical models in terms of accuracy. In [
180
], price forecasting is done using a variant
of Auto Regressive Moving Average Model (ARMAX), known as Hilbertian ARMAX, which
uses the exogenous variables. The functional parameters used in the proposed model are
designed as the linear combinations of the sigmoid functions. These parameters are then
optimized using a Quasi Newton (QN) algorithm. In [
181
], CNN and LSTM are used for
price forecasting in PJM electricity market. In [
182
], Deep Neural Network (DNN) is used to
extract the complex patterns from the price dataset of Belgium. In [
183
], Grey Correlation
Analysis (GCA) is used along with Kernel Principal Component Analysis (KPCA) to deal
with the dimensionality reduction issue. For prediction, SVM is used in combination with
Differential Evolution (DE), where DE is used to tune the parameters of SVM. In [
184
],
a variant of autoencoder is used, which comprises of encoder and decoder. First, the data
is encoded to deal with space complexity. Once the output is obtained, it is decoded into
original form. The authors in [
185
] implement an enhanced form of Artificial Bee Colony
(ABC) algorithm, known as Time Varying Coefficients Artifical Bee Colony (TVC-ABC),
for parameter tuning of Nonlinear Least Square SVM (NLS-SVM). The inputs are first fed to
ARIMA and then the output of ARIMA is given to NLS-SVM. This ARIMA plus TVC-ABC
NLS-SVM is a Multi Input Multi Output (MIMO) forecast engine.
The limitations of gradient descent methods led researchers to evolve ELM based upon local
minima, learning rate, stopping condition and iterations of learning [
186
]. ELM performs
differently as compared to the traditional learning algorithms because it gives comparatively
less forecasting error and better generalization performance in [
187
]. Different versions of
ELM have also been proposed by researchers like Kernel Based Extreme Learning Machine
(KELM). Robust classification is also performed in literature inspired by Mercer condi-
tion [
188
]. Moving ahead, solving the DEM problem at MG level in the Energy Internet
using both game theory and data analytics techniques is the modern trend. There is a recent
surge in data analytics that introduces various mathematical tools to address the uncertainties
[
189
]. While managing energy, two main methodologies are used: stochastic optimization
and robust optimization. These are widely used to handle data uncertainties [
190
]. Stochastic
optimization in energy management solutions is considered as one of the effective techniques
14
for the optimization of statistical objective function. The undefined numerical data has
to be assumed in order to follow a well-known probability distribution. Real time energy
management techniques that utilize stochastic optimization are proposed to reduce the op-
erational cost of MG [
191
]-[
195
]. In [
196
], a multistage framework is proposed in order
to reduce the cost of the energy system based on stochastic optimization. In order to cater
multi dimensional energy management, stochastic dynamic programming methods are used
[
197
]-[
200
]. Nonetheless, the precise calculation of probability distribution of uncertain
data causes tremendous challenges in practical applications, which include the complex
operations and constraints. Optimality performance is also affected by the impact of data
uncertainties. Such issues are not dealt in the existing energy management approaches that
contain stochastic optimization.
Robust optimization based energy management approaches rely on limited information and
enable distribution free models for handling the uncertainties [
201
]-[
206
]. In the optimized
energy management systems, the worst case operational scenarios are considered. Besides,
optimized energy management approaches help in removing the negative impact of the
uncertainties faced while achieving optimal performance. Thus, a novel pricing strategy is
proposed, which promotes robustness against uncertainties of power inputs [
207
]. Nonethe-
less, the robust version of controllable electric load management is ensured to be intractable
that relies on appropriate designs for objective function modeling and building of uncertainty
set.
Ever since the growing advancement in the field of ICT, a large amount of data is collected
regarding consumers’ behavior, states of battery, substations, customer devices, distributed
energy resources, renewable output, weather conditions, video surveillance, etc., [
208
]. The
energy generation using PVC is considered to be one of the important components of electric-
ity sector. It is also gaining attention of government because of decreasing environmental
issues and being cost effective. In [
209
], it is stated that PVC energy will be responsible for
16% of the total energy generation. PVC energy relies on solar radiations, which exhibits the
intermittent nature due to the position of sun and the movement of clouds that causes varia-
tions throughout the day. In addition, information of solar energy is the greatest concern both
for operators and planners of electrical systems. Hence, there is a sheer need of forecasting
PVC power generation [
210
] using machine learning techniques. The forecasting models that
are mainly used for prediction of electrical load and the RES are grouped in three categories:
statistical models like Auto Regressive (AR), Exponential Smoothing (ES) models [
211
]; AI
models like NN, CNN, and hybrid models like neuro fuzzy models [
212
,
213
]. As a case
15
study, electricity demand of 10 countries are taken into account in order to analyze these
methods. In [
214
], different forecasting techniques are used like multi-model, iterative, single
model multi-variate forecasting are analyzed in detail. It also covers different issues like
NN designing, implementation and validation. In [
215
], a combination of NN and enhanced
PSO are used in order to perform power forecasting, which focuses on feature selection. The
time-series of power generated by PVC contains many well defined patterns, such as peaks
at afternoon and off peaks in morning and evening. Subsequently, there is no generation
during the night. Therefore, time-series patterns can not be ignored [
216
]. ARIMA is used for
uni-variate time-series forecasting because solar generation tends to follow specific patterns
and it is proven to be efficient on the account of flexibility. It also performs its orderly
searching at every level. Thus, it determines the best fit model for particular time-series. The
optimization of parameters has significant impact on the performance of forecasting algo-
rithms. Different meta heuristic techniques are applied for forecasting purposes like ARIMA
[
217
], NN [
218
], SVR [
219
] in literature for improving performance. Game theory concept
is widely applied on MG energy management studies. It provides distributed self organizing
and self optimizing solutions for the problems having conflicting objective functions. In a
broader aspect, game theory is characterized in two categories in context of players. The
first category includes the players that have a binding agreement between them. While the
second category includes those players who are not having binding agreement between them
[
95
]. In non-cooperative game theory, the main focus is on predicting individual strategies
and it also assesses the players that make decisions to find NE. It provides a framework for
performing analysis, i.e., DEM, which is devised for characterizing the interaction between
players and decision making process to achieve NE. The strategic outcome between players
can be improved under mutual commitment.
In order to handle non-cooperative game theory based energy management, a multi user
based Stackelberg game is used to optimize the payoff of each player [
220
]. In [
221
], a multi
stage market model is proposed, which is based on cooperative game to reduce the cost of
utility. However, it maximizes the total profit of the market. In order to solve the dispatch
problem in integration of renewable resources generation and energy storage, a cooperative
distributed energy scheduling algorithm is proposed in [222].
Future events are determined by time-series forecasting. It is based on a series of given
historical events. In [
223
], building’s energy consumption is forecasted using linear regression
in the year 1980. The authors presented regression based forecasting technique that forecasts
16
residential energy consumption in Canada [
224
]. In 2012, a regression model was developed
to forecast load in banking sector in Spain [225].
There are two types of learning techniques in the literature: machine learning techniques
and deep learning techniques. The machine learning techniques are used in various fields
for forecasting purposes. They perform the best in pattern recognition as well. In [
61
], a
time-series of wind speed is used for its prediction along with the parameter optimization. It
includes two evolutionary algorithms: ANN and SVM. In a couple of decades, several hybrid
machine learning and deep learning techniques are introduced to improve the prediction
accuracy [
62
]. Gunay et al. proposed a hybrid of regression model and ANN in order to
forecast the electricity demand of Turkey [
63
]. It mainly works using a multi-variate dataset
where a regression model is used for feature selection. Afterwards, the selected features
are forwarded to the ANN for annual electricity demand forecasting. The approximation
capability of ANN is proven to be very effective. However, there are certain limitations,
such as overfitting and high computational time. Therefore, in literature, ANN is used in
hybrid models with other regression models, such as SVM [
226
]. Each model possesses
certain pros and cons, which cannot be overlooked. Neural networks such as MLP give
accurate forecasting results in case of the complex and non-linear scenarios. However, the
computational time is high, which consequently increases the overall cost of the system.
Therefore, it is the need of the hour to propose the hybrid techniques, which use ANN
for forecasting the complex and non-linear characteristics of the time-series. Whereas,
linear and stationary characteristics need to be forecasted using the techniques that consume
less computational resources, such as SVM. The characteristics of time-series data can be
decomposed and separated with the help of signal decomposition techniques [
227
]. ANN
neither performs feature engineering nor requires hand crafted feature selection. It rather
requires intensive domain knowledge, which makes it a difficult choice. Therefore, it is
required to incorporate deep learning techniques such as CNN and LSTM. These techniques
are considered as the building blocks of deep learning models [
67
], [
228
]. Various variants
of these building blocks have been proposed in order to enhance the network flexibility like
BiLSTM [
68
]. However, BiLSTM has an inherited issue of poor generalization. Therefore,
an ensemble strategy, which takes the average weights of a multi-model BiLSTM, is used in
the proposed work.
The aforementioned literature contains certain drawbacks. The researchers are using the same
learning model for time-series forecasting. However, it is a known fact that different models
17
handle volatility and variability of the data in different ways. Therefore, a large number
of researchers are focusing on the concept of decomposing the time-series into multiple
sub-series. So that the sub-series forecasting can be handled separately by adjusting the
models. These decomposition techniques are proven to be efficient in forecasting as compared
to the sole forecasting techniques [69].
The electricity load time-series shows fluctuations in intensities. Different techniques have
been introduced in order to analyze different levels of data intensities like wavelet method
[
229
]-[
233
], Fourier transform [
234
]-[
238
], EMD and EEMD [
70
]. Among the ensemble
methods, divide and conquer model is considered to be the best. Where, the actual time-series
is decomposed into multiple orthonormal sub-series on the basis of time domain. It helps
in removing the noise as well as in extracting high and low intensity based sub-series. All
of these techniques are applied on different time-series data and the findings reveal that
EEMD, which is a modified form of EMD, performs well in terms of acquiring forecasting
accuracy. In EMD, the time-series signal is decomposed in a number of Intrinsic Mode
Functions (IMFs) and residue. These obtained IMFs and residue by EMD reveal different
characteristics. EMD faces the mode-mixing problem while decomposition of the signal
takes place. Mode-mixing problem is either defined as a single IMF that contains multiple
characteristics or representation of same kind of characteristics by different IMFs. It makes
the interpretation of sub-series difficult. Therefore, EEMD is brought to the light by Wu
[
239
], which is a noise assisted data preprocessing technique that adds white noise to the
time-series. Furthermore, it also helps in the alleviation of mode-mixing problem. In [
240
],
the EMD signal decomposition technique is used to decompose the signal into multiple
sub-series and residue. The extracted sub-series are forecasted using LSTM forecasting
algorithm. There is a drawback that sub-series are not classified separately according to
linearity and non-linearity. Consequently, forecasting becomes extensively time consuming.
In the proposed work, EEMD is integrated with EBiLSTM and SVM. EBiLSTM is a deep
learning model that handles the complex and non-linear data efficiently. However, it has
high computational overhead. In order to reduce the computational time, linear IMFs are
forecasted using SVM as it shows good accuracy in order to learn linear data.
Table 2.1: Summary of related work
Objectives Forecast
Horizons
Data Resolution Datasets Techniques
18
Load forecasting
[36]
Short-term
Hourly building
cooling loads and
the meteorologi-
cal data
TRNSYS simula-
tion tool for elec-
tric load demand
data of Northeast
China
ANN and ensem-
ble method
Load Forecast-
ing [40]
Short-term
Monthly average
load demand
Assam north-
eastern state of
India data
GOA and SVM
Load forecasting
[45]
Long-term Yearly data
China electricity
data
SVM
Load forecasting
[60]
Short-term
and mid term
Half hourly data AEMO data EEMD-DBN
Wind speed fore-
casting [61]
Short-term
Hourly wind
speed time-series
data
Two wind speed
time-series data
EEMD with
adaptive noise
ANN
Load forecasting
[99]
Short-term
Hourly historical
data
Provincial capital
of Soria data
ANN
Load Forecast-
ing [100]
Short-term
Daily and hourly
data
PJM data EEMD-LSTM
Price and load
forecasting
[101]
Short-term
and mid term
Hourly data
NYISO and PJM
data
ESAE, NARX
and DE-ELM
Load forecasting
[102]
Very short-
term and mid
term
Hourly data EUNITE data
EEMD and
LSTM
Price forecasting
[103]
Long-term Hourly data
China’s power
grid investment
data
EMD, GA-SVM
and Radial basis
function ANN
Load forecasting
[119]
Short-term
Half hourly resi-
dential data
Data of 10,000
different cus-
tomers in NSW
LSTM
19
Load forecasting
[120]
Short-term
and mid term
Electricity hourly
load datasets that
contain daily av-
erage load
PJM data EEMD-SVR
Bayesian, MLP
[159]
UKDale UK
Forecasting
done using
behavioural
analytic
Requires inten-
sive training
MLR, BaggedT,
NN [160]
Beijing Beijing, China
Comparison
between tech-
niques done to
overcome the
limitations
Not suitable for
long term fore-
casting
DRN [161] ISO-NE New England
Load forecast-
ing done using
weather data
Overfitting
RBM,
ReLU [162]
Korea
Electric
Company
Korea
Two stage
forecasting
performed
Long-term fore-
casting not sup-
ported
DWT-IR, SVM,
SWA [163]
NYISO,
AEMO
Australia, US
Dimensionality
reduction and
paramater opti-
mization done
Time complexity
Modified MI,
ANN [164]
PJM US
Two stage fore-
casting is done
Time complexity
DAE [165] Hong Kong Hong Kong
Cooling load pre-
diction done
Time and space
consuming
Pooling Deep
RNN [166]
IRISH Ireland
Pooling of con-
sumers done
for aggregated
prediction
Difficult to train
20
ELM [167,168] USF US
Long, medium
and short-term
forecasting done
Overfitting
SLFN [
169
,
174]
Marine
Resources
Division
Australia
Optimization of
weights
Overfitting by
using moore-
penrose inverse
Sparse Bayesian
ELM [175]
Harvard
medical
college
USA
Optimization of
weights and bi-
ases using BP
Require intensive
training
PSO,
DPSO [176]
US USA
Compact ANNs
are produced
Large computa-
tional time
GWO with
NN [177]
Load USA
Weights and bi-
ases optimization
Time complexity
RELM [178]
Bench mark
UCI machine
Portugal
Use of context
neurons
Computationally
expensive
LSTM, DNN,
GRU [179]
EPEX Belgium
Comparison
between different
models
Overfitting
Hilbertian, AR-
MAX [180]
EPEX Spain, Germany
Optimization of
functional param-
eters for price
forecasting
Non-linearity
CNN,
LSTM [181]
PJM US
Two NNs are
used for price
forecasting
Computationally
expensive
DNN [182] EPEX Belgium
Complex patterns
are extracted for
prediction
Space complex-
ity
GCA, KPCA,
DE-SVM [183]
ISO NE-CA New England
Dimensionality
reduction is
removed using
hybrid of KPCA
and GCA
Overfitting
21
SDA [184] MISO
Arkansas, Texas
and Indiana
Variant of autoen-
coder used
Computationally
expensive
ARIMA, TVC-
ABC, NLS-
SVM [185]
PJM, NY-
ISO, AEMO
Australia, US
Parameter tuning
of SVM done us-
ing TV-ABC
Computationally
expensive
ANN with
meta heuristics
optimization
methods [186]
Load and
price/Com-
mercial load
of building
in China, Tai-
wan regional
electricity
load
Taiwan
Various para-
mater calcula-
tions done for
accuracy
Accuracy of mod-
els depend on na-
ture of dataset
OS-ELM with
kernel [187]
Load and
price/Sylva
bench mark
US
Comparison
of different
algorithms done
Restrict to the
computation of
streamed data
ELM in multi
class sce-
nario [188]
Load and
price/Uni-
versity of
California
Irvine
Canada
Robust classifica-
tion
High computa-
tional cost
Many studies are conducted in literature for EC forecasting, which are based on statistical,
machine learning and deep learning models. There are several techniques based on these
models that are proposed by researchers in the course of time. These techniques include
Auto-Regressive Integrated Moving Average (ARIMA) [
226
], LR [
241
], SVM [
242
], ANN
[
243
], sequence to sequence learning [
244
], DANN [
245
], etc. Table 2.1 presents the main
findings of literature review in terms of proposed schemes, contributions, performance
metrics, results and limitations. According to the statistics provided in [
246
] regarding the
application of these forecasting techniques in research, ANN is being used up to 47%, SVM
25%, Decision Tree (DT) 4% and the rest of the techniques are employed up to 25% in the
literature. The forecasting of residential electricity load is performed using ANN in [
127
].
However, the effectiveness of ANN increases as per the availability of different exogenous
22
variables like weather data, temperature, wind speed and humidity. The nonavailability of
dependent variables adversely affects the performance of ANN. RNN resolves this issue with
the help of BP in the network. It yields promising results while forecasting sequence-based
time-series. However, long-term dependencies are hard to learn by traditional RNN as it faces
the vanishing gradient issues due to time depth and single hidden layer. In order to overcome
these issues, the LSTM forecasting technique was proposed in 1997 [
129
]. It combines
short-term memory with long-term memory by incorporating three memory gates, which
save the long-term dependencies in them. In [
247
], LSTM is combined with GA in order to
optimize the model’s hyper-parameters. The comparison between different deep learning and
machine learning models is performed in [
248
], which proves that the performance of deep
learning models is reasonably better as compared to the machine learning models. DANN is
used as the forecasting technique in [
128
]. It is a multichannel deep convolution network,
which uses the variables’ data to detect latent features. The extracted latent features are fed to
MLP for forecasting the time-series data. However, MLP performs poorly when detecting
temporal dependencies. In [249]-[253], deep LSTM is used in order to perform forecasting.
Various variants of LSTM are proposed in the literature that include DLSTM. It uses skip
connections that are extracted from the concept of ResNet [
161
]. Moreover, DLSTM enhances
the efficiency of the network by reducing the vanishing gradient issue [
254
]. BiLSTM is used
in literature to learn two-way dependencies of the data: past and future [
255
]. GRU is an
advanced version of LSTM that combines the input gate and forget gate of the network into
one gate, termed as an update gate [
256
]. Multi GRU is also a variant of GRU that is used to
optimize the electricity dispatch plan [
257
]. In [
258
], GRU is used in order to forecast PV
power generation. Apart from time series sequence data, there are other high dimensional
information as well like spatiotemporal matrix that exists in the time-series, which can not
be learned by GRU. Therefore, it is required to add such features in GRU that can optimally
learn the high dimensional temporal data. In deep learning methods, feature extraction is
ideally performed by CNN. It is widely used in the field of image and pattern recognition. It
also learns local features that are based on a strong relationship between nearby points [
259
].
Researchers in the fields of image recognition, speech recognition and emotion recognition
frequently use CNN. It is an end-to-end learning model that simultaneously covers spatial and
temporal trends [
260
]. Moreover, CNN-LSTM is used in speech recognition to learn global
and local emotion-based features. Multiple blocks are used in the network, comprising of
convolution block and pooling block that learn the local correlation in the data. LSTM learns