Applications of big data and artificial intelligence in smart energy systems. Volume 1. : smart energy system : design and its state-of-the art technologies / edited by Neelu Nagpal [and four others].

Format:

Book

Contributor:

Nagpal, Neelu, editor.

Series:

River Publishers Series in Computing and Information Science and Technology Series

Language:

English

Subjects (All):

Artificial intelligence--Engineering applications.

Artificial intelligence.

Smart power grids.

Physical Description:

1 online resource (318 pages)

Place of Publication:

Gistrup, Denmark : River Publishers, [2023]

Summary:

This book covers the applications of various big data analytics, artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management.

Contents:

Cover

Half Title

Series Page

Title Page

Copyright Page

Table of Contents

Preface

List of Figures

List of Tables

List of Contributors

List of Abbreviations

Chapter 1: Introduction to Smart Energy Systems in Recent Trends

1.1: Introduction

1.2: Global Emission

1.3: Evolution for Clean Energy: A Transition

1.4: Smart Energy System

1.5: Internet of Things (IoT) for Smart and Sustainable Future

1.5.1: IoT in smart city

1.5.2: IoT in agriculture

1.5.3: IoT in healthcare

1.5.4: IoT in smart grid and power management system

1.6: Recent Developments in Smart Energy Systems

1.7: Conclusion and Future Measures to be Considered

Chapter 2: An Overview of Artificial Intelligence, Big Data, and Internet of Things for Future Energy Systems

2.1: Introduction

2.2: Related Work

2.3: Sectors Involved in Energy Need Big Data, AI, and IoT

2.3.1: The energy sector requires big data

2.3.2: Big data techniques

2.3.3: Data communication techniques

2.3.4: Techniques for analyzing data

2.3.5: Data analytics techniques in smart grid

2.3.6: Mining data from a power system

2.3.7: Modern grid systems and power consumption advanced analytics

2.4: Supporting Power Usage with IoT and Artificial Intelligence

2.4.1: The need for artificial intelligence in the renewable energy industry

2.4.2: Artificial intelligence (AI) research techniques classification

2.4.2.1: The use of computer-assisted learning systems

2.4.2.2: Fuzzy logic

2.4.2.3: Computer-aided translation

2.4.2.4: Robotics

2.4.2.5: Need of robotics in the energy sector

2.4.3: The energy sector requires IoT

2.5: Role of IoT in Energy Sectors

2.5.1: IoT Impacts for the energy sector

2.5.2: Internet of Things applications in energy policy, economics, and production.

2.5.2.1: Sectors of regulation and the market

2.5.2.2: Energy supply sector

2.5.2.3: Power transmission grids or energy grids

2.5.2.4: Energy demand sector

2.6: Future Energy Systems' Unsolved Problems

2.6.1: IoT energy sector challenges

2.6.2: Open challenges in AI energy sector

2.6.3: Open data analytics challenges in energy

2.7: Conclusion

Chapter 3: LoRa: A New Technology for Smart Grid Applications

3.1: Introduction

3.2: Related Work and Background

3.3: LoRa Challenges

3.4: LoRa Applications

3.5: Characteristics and Future Direction of LoRa Technology

3.5.1: Communication Range:

3.5.2: Placement of Multiple Gateways:

3.5.3: Link Coordination:

3.5.4: Security

3.5.5: Big data and AI

3.6: Case Study

3.7: Conclusion

Chapter 4: Clustering Hybrid Application for Load Forecasting in Smart Grids

4.1: Introduction

4.1.1: Dataset pre-processing

4.1.2: Clustering techniques

4.1.2.1: K-means

4.1.2.2: Partitioning around medoids (PAM)

4.1.2.3: Gaussian mixture model (GMM)

4.1.3: Autoencoder

4.1.4: Forecast

4.2: Methodology

4.2.1: Clustering hyperparameters

4.2.2: LSTM hyperparameters

4.3: Case Study

4.4: Results and Discussion

4.4.1: Results for LSTM hyperparameter

4.4.2: Cluster results

4.5: Conclusion

Chapter 5: Big Data Analytical Techniques for Electrical Energy Forecasting in Smart Grid Paradigm

5.1: Introduction

5.2: Attribute Selection Techniques

5.2.1: Filter method

5.2.2: Wrapper method

5.2.3: Embedded method

5.2.4: Heuristic method

5.3: Statistical Characterization of Data

5.4: Machine Learning Techniques of Forecasting

5.4.1: Linear regression (LR)

5.4.2: Polynomial regression (PR)

5.4.3: Ridge regression (RR)

5.4.4: LASSO regression

5.4.5: Decision tree regression (DTR).

5.4.6: Random forest regression (RFR)

5.4.7: K-nearest neighbor regression (KNNR)

5.5: Comparative Analysis of Machine Learning Techniques

5.6: Renewable Energy Forecasting

5.7: Conclusions and Discussion

Chapter 6: A Review of Smart Grid Planning Approaches and a New Proposal for Operational Planning of Smart Grid with Smart Homes

6.1: Introduction

6.2: Literature Review on Smart Grid Planning

6.3: Research Gaps and Motivations Behind a New Proposal

6.4: Proposed Smart Grid Planning Approach

6.4.1: Operation of smart grid with smart homes during time-varying load, PV generation, and electricity price

6.4.1.1: Time-varying load

6.4.1.2: Time-varying PV generation

6.4.1.3: Time-varying electricity price

6.4.1.4: Operation of smart homes with RPV during time-varying load, PV generation, and electricity price

6.4.2: Operational optimization problem

6.4.3: Solution strategy

6.4.3.1: PSO algorithm

6.4.3.2: Forward−backward sweep load flow algorithm

6.4.3.3: Incorporation of PV generation model in forward−backward sweep load flow algorithm

6.4.3.4: Overall operational planning algorithm

6.4.4: Simulation results and discussion

6.4.4.1: Determination of number of electricity consumers and prosumers

6.4.4.2: Smart home PV generation profiles

6.4.4.3: Hourly aggregated PV generation injection

6.4.4.4: Hourly energy loss and minimum bus voltage magnitude

6.4.4.5: Performance comparison of PSO algorithm with DE algorithm

6.5: Conclusion

Chapter 7: Smart Meter Data Analytics Case Study: Identification of LV Distribution Network Topology to Design Optimal Planning Solutions

7.1: Overview

7.2: Notation

7.3: Distribution network model

7.4: LV network identification using smart meter data

7.4.1: Single-phase topology identification algorithm.

7.4.1.1: Topology Estimation

7.4.1.2: Topology Validation

7.4.1.3: Hidden Node Detection

7.4.2: Three-phase topology formation

7.5: Probabilistic LV network hosting capacity assessment under uncertainty

7.5.1: Hosting capacity assessment algorithm

7.6: Results

7.6.1: LV Network identification using smart meter data

7.6.2: Probabilistic LV network hosting capacity assessment under uncertainty

7.6.2.1: Capacity assessment for photovoltaic panels

7.6.2.2: Capacity assessment for electric vehicles

7.6.3: Discussions

7.7: About the authors

Chapter 8: Build Smart Grids on Artificial Intelligence − A Real-world Example

8.1: Introduction

8.2: The AI Application Platform - FNET/GridEye

8.3: Event Identification Based on AI

8.4: AI-based Inertia Estimation using Ambient Synchrophasor Measurement

8.5: AI-based Event Location and Magnitude Estimation

8.6: Model-free Data Authentication using AI

8.7: AI-based Frequency Control

8.8: Stability Prediction Based on AI

8.9: Conclusion

Chapter 9: Artificial Intelligence Enabled Energy-efficient Technologies for Secured Smart Homes

9.1: Introduction

9.2: Iot-Based Smart Home System

9.3: Smart Home Technologies

9.3.1: Smart Home Requirements

9.3.1.1: Heterogeneity

9.3.1.2: Self-Configurable

9.3.1.3: Extensibility

9.3.1.4: Context Awareness

9.3.1.5: Usability

9.3.1.6: Security And Privacy Protection

9.3.1.7: Intelligence

9.3.2: Smart Home Benefits

9.4: Smart Home Energy-Efficient Optimization Techniques

9.5: AI-based Iot Optimization System

9.6: Smart Home Security Concerns

9.6.1: Threats Posed In A Smart Home System

9.6.1.1: Application Layer

9.6.1.2: Network Layer

9.6.1.3: Middleware Layer

9.6.1.4: Perception Layer

9.7: Case-Based Study: Survey On Smart Energy Efficient Homes.

9.8: Conclusion

Chapter 10: A Review of Technologies in Net Zero Energy Building for Islanded Operation

10.1: Introduction

10.2: Hybrid Renewable Energy Systems

10.2.1: System cost calculation methods

10.2.2: System reliability indicator

10.3: Semi-transparent Photovoltaic Cells

10.3.1: Transparency of solar cells

10.3.2: Types of semi-transparent solar cells

10.3.3: Carbon-nanotube layering

10.4: Applications of IoT for efficient Efficient Operations

10.4.1: Applications of IoT for optimized energy usage in smart buildings

10.4.2: Data transmission in IoT connected networks

10.5: Next-generation HVAC Systems

10.5.1: Parameters for efficient HVAC performance

10.5.2: Methodologies

10.6: Simulation and Comparison

10.7: Conclusion

Index

About the Editors.

Notes:

Includes index.

Description based on print version record.

ISBN:

1-5231-5638-4

1-00-344071-1

1-003-44071-1

1-000-96383-7

1-000-96382-9

87-7022-824-8

9781003440710

OCLC:

1391443186