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Hybrid Energy Systems for Offshore Applications Ibrahim Dincer
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Library of Congress Cataloging-in-Publication Data
ISBN 9781119821243
Cover image: Electrical Tower, Patrick Daxenbichler | Internet of Things, Monthira Yodtiwong | Dreamstime.com
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4
M.S. Kumaravel, N. Alagumurthi and P. Mathiyalagan
4.3.3
4.3.4
M. Balamurugan,
Revathi and
9
Deepica S., S. Kalavathi, Angelin Blessy J. and D. Maria Manuel Vianny
N. Padmapriya, T. Ananth Kumar, R. Aswini, R. Rajmohan,
Usharani,
12.4
12.3.2
G. Vallathan, Senthilkumar Meyyappan and T. Rajani
List of Contributors
Shihabudheen KV
Sheik Mohammed S
N.M. Balamurugan
TKS Rathish babu
K Maithili
M. Adimoolam
Praveen Mishra
M. Sivaram
A. Daniel
Raju Ranjan
M.S. Kumaravel
N. Alagumurthi
Mathiyalagan
Balmukund Kumar
Aashish Kumar Bohre
M. Pavithra
R. Rajmohan
T. Ananth Kumar
S. Usharani
P. Manju Bala
N. Revathi
R. Gayathri
S. Sathya
G. Karthi
A. Suresh Kumar
S. Prakash
Deepica S.
S. Kalavathi
Angelin Blessy J.
D. Maria Manuel Vianny
G. Rajakumar
xiv List of Contributors
G. Gnana Jenifer
T. S. Arun Samuel
N. Padmapriya
R. Aswini
P. Kanimozhi
R. Indrakumari
G. Vallathan
Senthilkumar Meyyappan
T. Rajani
Pedram Asef
Preface
This book aims to primarily address issues surrounding the optimization, consumption, and management of energy resources with the use of hybrid intelligence techniques. The consumption and optimization of energy play a crucial role in sustaining the development goals of modern society. The need to save energy while reducing its overall cost cannot be emphasized enough. In recent times, smart computing technologies have slowly but inevitably replaced the traditional computational methods in energy optimization and consumption and its optimal scheduling and usage. Smart computing has permeated almost all areas of technological innovation today. Smart computation techniques such as artificial intelligence, machine learning, deep learning, and IoT have become indispensable to designing and building applications that span diverse areas like distributed environments, healthcare, smart cities, agriculture, and a host of other functional areas. Hence, it is no surprise that in power usage, these smart computation techniques have been incorporated along with traditional computation and scheduling methods to bring about optimal reductions. This book is predominantly focused on emerging concepts and algorithmic approaches in machine learning and artificial intelligence to bring about enhancements in soft computing techniques in energy optimization. This site will contribute to research work undertaken by researchers, academicians, data scientists, and technology developers alike.
This book comprises of fifteen chapters, each chapter elaborating upon various aspects and techniques related to optimizing and managing energy. Chapter 1 elaborates upon the review and analysis of machine learning-based techniques for load forecasting. A load-forecasting algorithm for time-series loads using AI techniques with supervised methods is presented and discussed. This includes a comparative assessment of load forecasting based on supervised artificial intelligent algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Extreme Learning Machine (ELM), which is performed on smart meter
data. The results are presented in this chapter, along with a performance analysis of the selected algorithms.
Chapter 2 elaborates on various energy optimization techniques, examines how energy can be optimized, and what techniques are available and used in cloud and fog computing. Chapter 3 is focused on the energy efficiencies of various next-generation cloud computing techniques. This chapter delves deeper into the benefits and trade-offs arising from the adoption of various energy conservation measures. It outlines the challenges and recommendations to be considered in the future. Chapter 4 presents a method of energy optimization using a Silicon dioxide composite and an analysis of wire electrical discharge machining characteristics. Here, a stir casting method to produce the uniform distribution of SiO2 particles in an Aluminum matrix composite (AMC) is expanded upon, along with confirmation by microstructural analysis during which the XRD pattern reveals that the SiO2 indeed has a monoclinic crystalline structure.
Chapter 5 presents a discussion on optimal planning of renewable DG and the reconfiguration of the distribution network that considers multiple objectives using the PSO technique under different scenarios. This chapter discusses the methods for improving voltage and reliability and reducing power losses (active and reactive) and proposes an IEEE-69-bus test distribution network with separate scenarios for optimal sizing-siting of multi-renewable DGs with the reconfiguration of the particle swarm optimization (PSO) technique. Chapter 6 propounds upon energy optimization for spectrum sensing in Distributed Cooperative IoT Networks using deep learning techniques. This chapter further investigates applying deep learning methods to the Internet of Things (IoT) applications while focusing on energy optimization mechanisms.
Chapter 7 expands upon the energy optimization for a road network using IoT and deep learning methods. In this chapter, an overview of the components of an intelligent road transportation system is first presented. It is then followed by a discussion on the potential for leveraging the advancements in deep learning and Internet-of-Things (IoT) in modeling the short-term traffic states for the prediction of traffic flow and energy optimization in using fuel and electricity consumption and road lighting. Chapter 8 explores the means of energy optimization in smart homes and buildings. In this chapter, information about communication technologies that enable energy optimization is presented. Such optimizations in smart homes and buildings are proposed using energy management and optimal scheduling by utilizing the Internet of Things and smart grids. This chapter further touches upon methodologies considered and used in the study and presents recommendations for the future direction of the work.
Chapter 9 focuses on machine learning-based approaches in energy management for spurring the smart city revolution. This chapter proposes a machine algorithm for optimizing the energy sources in a smart city. Such an approach can benefit future smart city plans and help adequately deal with existing energy resources. Chapter 10 similarly explores the design of an energy efficient IoT system in the management of poultry farms. It proposes a technique for enabling efficient poultry farming that will cause increased production and profits. It propounds the use of solar technologies for generating electricity, which, apart from being used in households, have also been successfully implemented in commercial farms to make use of energy-saving batteries and the opportunity to sell the power back to the grid.
Chapter 11 considers IoT-based energy optimization methods that use artificial intelligence for enabling smart farming. This chapter outlines how IoT can contribute to the improvement of agricultural productivity for reaching new sustainability heights. The first part of the chapter introduces the many ways of using IoT in smart farming and its manifold benefits. The second part elaborates on revolutionizing the farming process by using Artificial Intelligence. The third part emphasizes energy optimization in agriculture, in general, using IoT and Artificial Intelligence. Chapter 12 proposes the use of smart energy management techniques in industries. It elaborates upon the concept of energy consumption and control to effectively regulate energy demands using smart grids in manufacturing industries. It also offers a description and details of the statistical method that can be tailored to the specific needs of manufacturing customers for building energy-efficient power systems.
Chapter 13 reviews energy optimization techniques using soft computing in telemedicine. This chapter proposes an expert system based on fuzzy rules that can be used to calculate patient risk levels. This is followed by discussing various energy optimization techniques using the Internet of Things, machine learning, and deep learning techniques in Chapter 14. These techniques are used in different applications and for various optimization. The Internet of Things is connected to multiple technologies such as Cyber-Physical Systems, Big Data Management, Cloud Management, etc. Using IoT connectivity continuously to monitor energy usage and optimization in various devices and energy consumption can be reduced in several industrial and manufacturing fields, leading to many benefits, including environmental benefits such as reducing CO2 emissions.
Acknowledgements
We are deeply indebted to Almighty God for giving us this opportunity and it is only possible with the presence of God.
I want to thank the Almighty for giving me enough mental strength and belief in completing this work successfully. I thank my friends and family members for their help and support. I express my sincere thanks to the management of Galgotias University, Greater Noida, Uttar Pradesh, India. I wish to express my deep sense of gratitude and thanks to Wiley-Scrivener, publisher, for their valuable suggestions and encouragement.
John A. PhD
I sincerely thank my VIT management for continuous support and encouragement. I thank my family members and friends for their timely support and help. I extend my thanks to Wiley-Scrivener, publisher, for excellent support and guidance.
Senthilkuamr Mohan, PhD
I express my sincere thanks to the management of CTiF Global Capsule, Department of Business Development and Technology, Aarhus University, Birk Centerpark 15, Herning 7400, Denmark. Also, I would like to thank the Wiley-Scrivener Press for allowing me to edit this book.
P. Sanjeevikumar, PhD
I express my sincere thanks to the management of Abu Dhabi Polytechnic. Also, I would like to thank the Wiley-Scrivener Press for allowing me to edit this book.
Yasir Hamid, PhD
1
Review and Analysis of Machine Learning Based Techniques for Load Forecasting in Smart Grid System
Shihabudheen KV1 and Sheik Mohammed S2*
1Electrical Engineering Department, National Institute of Technology, Calicut, Kerala, India
2Electrical and Electronic Engineering Programme Area, Faculty of Engineering, Universiti Teknologi Brunei, Gadong, Brunei Darussalam
Abstract
Electrical load forecasting is an important process that can improve the efficiency and economy of the utility grid especially in the smart grid environment. Load forecasting plays a significant role in making decisions such as planning, generation scheduling, operation, pricing customer satisfaction, and system security. However, load forecasting is a tedious and difficult task due to the intermittent nature of Renewable Energy Systems (RES) that varies depending on the seasons and parameters such as change in temperature and humidity. Moreover, the connect loads are also complex in nature as they vary from season to season. Artificial Intelligent (AI) techniques are a promising approach for better load forecasting having chaotic and random variation of both load and generation. In this chapter, a load-forecasting algorithm for time series loads using AI techniques with supervised methods is presented and discussed. A comparative assessment of load forecasting based on supervised artificial intelligent algorithms, such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Extreme Learning Machine (ELM), is performed on smart meter data. The results are presented and performance of the selected algorithms are analysed.
Traditionally, electric grids are a network of electric power generation, transmission, and distribution systems controlled by a centralized system. The conventional electric grid system has one way power flow and a communication approach from the generating station until the end customer. However, the power generation, transmission, and distribution systems had a noteworthy revolution in recent times. The progress and development in power generation using renewable energy sources is one of the important reasons behind this transformation. The restructured power system has made distribution systems bi-directional, controllable grids called Smart Grids. The Smart Grid consists of a number of RES
Figure 1.1 Structure of smart grid [1].
with loads, ESS, sensors, and communications networks connected in a well-arranged fashion so that it has the potential to improve overall system performance. The coordinated and controlled operation of this integrated structure makes the grid smarter by managing generation, distribution, customers, and the market in both an efficient and effective manner. Figure 1.1 shows the different domains and stakeholders of the Smart Grid.
Electricity load forecasting is a projection of the load demand that electricity users are expected to have in the future. Load forecasts enable the utilities to manage supply and demand and also ensure the stability of power grids. Load forecasting is the key element for smart grid operation, as it plays a vital role in decision-making such as planning, scheduling, operation, capacity addition, pricing, generation planning, and system security. Another major advantage of load forecasting is that it helps both the utility and consumers to optimize their energy usage.
Load forecasting is classified based on horizon and scale. Scale level is the unit size at which the forecasting is performed. Scale level forecasting ranges from individual forecasting (meter-level) in homes and building levels (multi-meters level) to region, district, state, and up to country (integrated) level load forecasting. On the other hand, horizon defines the time range of load forecasting. Horizon level forecasting is classified as very short-term load forecasting (VSTLF), short-term load forecasting (STLF), medium-term load forecasting (MTLF) and long-term load forecasting (LTLF). In VSTLF, minutes to hour ahead prediction is carried out and SLTF is day ahead to weekly forecasting. MTLF deals with one week to three years prediction and more than three years prediction is known as LTLF. Each type of forecasting serves different purposes in the power system for scheduling, economic dispatch, operation planning, maintenance, capacity expansion planning, fuel economy, sales, etc. [2–4].
Traditionally, expected demand is forecast using the information about past use and other related data with the aid of charts and graphs by applying an engineering approach. Later, data driven approaches are predominantly used to build prediction algorithms to improve the efficiency and accuracy of forecasting. Statistical based techniques and intelligent computing are the two main categories of data driven approaches applied for electricity load forecasting [5, 6]. Statistical approaches use historical data to compare energy consumption with the most relevant variables as inputs. More high-quality historical data therefore plays a vital role in the efficacy of statistical models. Conventional approaches like Regression Models,
Conditional Demand Analysis (CDA), Auto Regressive Moving Average (ARMA), and ARIMA are the most commonly adopted statistical methods for time series prediction. However, many researchers have investigated forecasting using AI based techniques and deep learning and those techniques have become the widely accepted technology over the past decade [7]. In addition to that, machine learning techniques like Classification and Regression Trees (CART), as well as Support Vector Machine (SVM) techniques are also used for time series prediction. Fuzzy Logic Systems, Artificial Neural Networks (ANN), Evolutionary Programming, and expert systems are some of the AI based approaches. Among them, ANN has widespread acceptance for time series forecasting [8–12]. Many attempts are made to solve the load-forecasting problems using AI based hybrid approaches. A comprehensive review on all such types of forecasting techniques are discussed in [13–16]. A review based on different categorization of the various forecasting, including the hybrid method, is less attempted in most of the literature.
In this book chapter, an extensive review of different supervised AI based load forecasting methodology is discussed. The review includes different categories of prediction such as single prediction and hybrid prediction methods. The details of hybrid prediction such as combined AI based prediction and signal decomposition based prediction techniques are included. Moreover, a comparative simulation study is performed on smart meter data. Methodology of forecasting is described in Section 1.2. A comprehensive review of various AI based prediction strategies applied for load forecasting is presented in Section 1.3. Comparative assessment of single and hybrid predictions is performed on smart meter data and the results are presented and discussed in Section 1.4. AI techniques such as Back Propagation Based Neural Network (BPNN), Support Vector Machine (SVM), and Extreme Learning Machine (ELM) are used for single prediction. For hybrid prediction, Empirical Model Decomposition (EMD) based BPNN and SVM (EMD-BPNN and EMD-SVM) prediction are performed. The chapter is concluded in Section 1.5.
1.2 Forecasting Methodology
The forecasting methodology for prediction of time series loads consists of two steps, as shown in Figure 1.2. The first step of methodology is feature extraction. Initially, a sufficient quantity of features is extracted form load time series data. Feature extraction procures the features which aid in the prediction of time series data. Transferring the collected features into a
Figure 1.2 Overall steps for load time series forecasting methodology.
more informative analysis domain helps in sensing the hidden characteristics of future loads. The second step is the implementation of a predictor for accurate forecasting.
Let y(t) represent a load time series data. The prediction equation can be mathematically represented by
where g(t) indicates the extracted features, f represents the predictive function to be approximated by predictor and + yt k ˆ () is k step ahead of predicted values of y(t)
1.3
AI-Based Prediction Methods
Many AI-based prediction methods are proposed in literatures for time series based load forecasting. The classification of AI based prediction techniques is shown in Figure 1.3. An overview some of commonly used prediction methods are outlined in this section.
1.3.1
Single Prediction Methods
Single prediction indicates the prediction of time series, which is formed using a single AI technique. Some of the AI techniques used for single prediction of time series data are Linear Regression, Artificial Neural Networks, Support Vector Regression, Extreme Learning Machine, and Neuro-Fuzzy Methods.
1.3.1.1
Linear Regression
This approach is used to predict the dependent variable using several independent variables or features. It uses the assumption that a linear relation may exist between the features and output signals. A linear regression model is represented as
() is estimated load
total signal length. xk() i is input feature variables ai is parameters to be calculated
number of span.
The unknown parameters of the regression model are calculated by the least square estimation method [17]. A short term load forecasting with multiple linear regression was implemented in [18]. A medium term load
Figure 1.3 Classification of
forecasting strategy using multivariable linear regression is proposed in [19]. A lesser number of tuning parameters are required for linear regression forecasting problems, but handling the nonlinearities and uncertainties in the prediction is a tedious job [20].
1.3.1.2 Artificial Neural Networks (ANN)
Artificial Neural Network (ANN) is a stochastic based prediction technique inspired by the concept of biological neurons. ANN is a parallel computational algorithm where neurons are connected parallel through a definite pattern. There are three common ANN structures, Single-Layer Feed-Forward NN, Multi-Layer Feed-Forward NN and Recurrent NN. The Multi-Layer Feed-Forward NN, which consist of one input layer, one output layer, and one or more hidden layers between the input and output layers is the most commonly preferred ANN structure [21]. The number of neurons in the input and output layer is determined by the application, whereas the hidden layer neurons are fixed often based on trial and error methods [22]. Data provided to the input neuron is multiplied by an arbitrary connection weight and is summed to derive the net input to the neuron in the next layer. Once the combined weight from preceding neurons
Figure 1.4 Feed forward neural network with single hidden layer.
arrives at a particular neuron, an activation function at this particular neuron approximates the received input to known output. The architecture of a single hidden layered feed forward neural network is shown in Figure 1.4 and its input/output relation can be written as
where Ih is the number of neurons of the hidden layer, ∈ wR Ih and ∈ × vR nIh are the threshold of the output θw ∈ R and threshold of the hidden layer vector θ ∈ R v Ih .
The training of ANN can be performed by a well-known algorithm called the back propagation based gradient descent rule [23]. In [24], Park et al. proposed a multi-layered perceptron (MLP) based ANN model for efficient time series load forecasting. The Generalized Delta Rule (GDR) [25] based error back propagation algorithm is used for the training of parameters. A Hybrid Quantized Elman Neural Network (HQENN) is applied for short-term load forecasting using an extended quantum learning algorithm [26]. Most of the above works use a gradient rule to optimize parameters. A gradient based back propagation algorithm produces slow converges and suffers from local minima trapping.
1.3.1.3 Support Vector Regression (SVR)
In machine learning, Support Vector Machines (SVM) are the most popular algorithm based on kernel methods. SVM is gaining popularity compared to other soft computing techniques such as AN, as it overcomes the bottleneck issues of ANN, like over fitting and local minima. Support Vector Regression (SVR) [27] is an extended version of SVM that can be effectively used for regression problems. The architecture of SVR is shown in Figure 1.5. All the input combinations are transferred to a higher dimensional plane using a special function called kernel functions [28]. The input/output relation of SVR is written as
where w is the weight connected between input and output, b indicates the bias in the output node, and ϕ (x) is the transferred function in a higher dimensional space. The optimum value of weight and base can be obtained using a quadratic optimization technique. The output equation after simplification can be written as
