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Chapter One : Introduction
Background
Residential energy consumption accounts for a significant share of total energy consumption in the United States. The significant increase in energy consumption presents several problems for energy security and the environment [1].
Smart homes
Smart Home Application Areas
- Resource Management Applications
- Security Applications
- Health-care and Elderly-care Applications
- Activity Recognition
Smart home security schemes offer additional utilities such as fire and smoke detection, intruder detection, and home monitoring and control, in addition to protecting the home from intruders. Detection systems, cameras, security codes and other devices help ensure a smart home by identifying whether visitors are residents or intruders.
Smart Home Save Energy
- Smart Water Leak and Freeze Detectors
- Smart Thermostats
- Smart Light Bulbs
- Smart Plugs
- Smart Appliances
- Smart Home Security Systems
- Smart Sprinkler Systems
- Smart Garage Door Opener
If the user forgets to turn off the light, he or she can do it remotely. If the smart washing machine needs to be repaired, it can send an email to the user to notify him or her of the problem.
Home Energy Monitors
- Handheld monitors
- Online monitors
- Plug-in monitors
Smart garage door openers connect to smartphone applications and can notify the user when the door is open or closed, as well as remotely close and open the door. Being signaled that the garage door is open helps prevent burglaries and prevents the home from losing its heating and cooling efficiency due to a wide open garage door.
Benefits of an energy monitor
Energy monitor features
- Household vs. individual appliance monitors
- Appliance recognition
- Real-time cost tracking
- Mobile apps and notifications
- Solar ready monitor options
- Installation
Real-time cost monitoring allows the user to keep track of how much energy is consumed and how much it costs. This feature in energy monitors allows the user to see how much electricity the solar panels generate, when it is produced and how it is consumed.
Smart Infrastructure and Buildings
Smart Energy
14 Smart energy is a larger concept than any of the above energy sources, such as clean energy or traditional energy.
Smart Homes and Buildings
Smart Grid
Intelligence can be called the "internet of energy" model, which is based on the concept of intelligent energy production or more, smart grid, smart consumption and storage. 15 The electricity transmission module transmits the electricity produced in the power plants via transmission lines to other stations.
Smart Technology
Components of a Home Energy Management System
- The Central HEM Unit
- Sensing Devices
- Measuring Devices
- Smart Appliances
- ICT (Information and Communication Technology)
The central unit, to which all other modules are connected, is the most important aspect of the HEM system. When the HEM system detects a low temperature, it can, for example, adjust the overall temperature of the house or turn on the lights in a specific room if someone is there. The HEM system can be developed into an overall supervisor, monitoring all elements of the home and making it a safe and energy-efficient environment [83] by combining sensors ranging from energy use to security.
The numerous measuring instruments, in addition to the detection devices, are important components of the HEM system. However, there are cases where machines can participate in a HEMS configuration without the need for a smart meter.
Components of Smart Buildings
- Energy Efficiency
- Efficient Operations
- Occupant Comfort
- Energy-Efficient Smart Buildings
In the case of HEMS, all functions are generally managed by a software platform, with information and communication technology (ICT) serving as the link between the HEM central unit, measurement and sensing devices, and smart devices [92]. In addition, ICT takes care of all communication between the user and the system, enabling the user to perform energy-related activities. ICT also plays a key role in transferring data to the main unit for system improvement in smart systems where patterns, efficient load balancing and peak reduction are important.
The statistics show the operation of the building so far and the changes that need to be made. It can also boost the efficiency and experience of the occupant by offering location services.
Energy Frameworks
Demand Side Management
DSM is presented as a viable long-term solution to these problems by allowing demand-side consumers to take a more active role in adapting their energy consumption. The power industry has used various forms of DSM, such as incentive-based and price-based programs, with varying degrees of success. For example, price-based programs allow customers to shift their electricity consumption from peak to off-peak hours; humidity, air speed, air quality and other aspects must all be considered.
Smart House and their energy consumption optimization are becoming more popular as a result of this essential function.
Demand-side response in systems integrating renewable energy into supplies
And then there is the people-to-people element, which is the customer support element for those occasions when the user just needs some help.”. With so many new advancements happening to ensure the best possible upgrade of smart home energy systems, it's hard not to be optimistic about the industry's near future. The demand-side response gradually trickles down, as the user might say to the smaller customers.
Yet they will need to have these middlemen who will aggregate the demand and act on their behalf.”
Cloud Computing for Demand Side Management
Cloud computing is responsible for managing compute, storage and resources available on demand from the network and scheduling electricity for users. Furthermore, fog computing is a specific cloud computing architecture that deals with edge resource management for efficient resource management for users.
Machine Learning
- Types of Machine Learning
- Regression vs. Classification in Machine Learning
- Classification
- Machine Learning Classification Algorithms Types
- Logistic Regression
- Logistic Regression Types
A minimal amount of labeled data and a more significant amount of unlabeled data are used in this ML. It can help to train a supervised learning algorithm where there is insufficient labeled data. In the following step, the features of the labeled data are used to highlight the unlabeled information.
Regression algorithms are used to predict continuous values such as price, salary, age, etc., while classification algorithms are used to predict discrete values, such as male or female, right or wrong, spam or not spam, and so on. Since the environment of the objective or related variable is dichotomous, there are only two categories here.
Binary or Binomial
The LR supervised learning categorization technique is used to predict the stability of an object variable. In basic terms, the associated variable is a binary level, with data points labeled 1 or 0. LR represents a binary logistic regression with binary level variables, although it can also predict two additional types of object variables.
Multinomial
Ordinal
- K-Nearest Neighbours
- Naïve Bayes
- Decision Tree Classification
- Random Forest Classification
- Regression
- Regression Algorithm Types
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression
- Artificial neural networks
- Support-vector machines
- Fusion
- Fuzzy inference system
- Problem Statement
- Research Hypothesis and Philosophy
- Research Questions
- Objectives
- Thesis Structure
The following comparison chart of the linear and non-linear datasets will help us understand it better. The original purpose of the ANN technique was to solve problems in the same way as a human mind. Based on the stage of the merger process, data fusion, function fusion and decision fusion are three categories.
Decision-level data fusion and machine learning enable the determination of the data patterns of each sensor, the decision-making process and the integration of all final decisions by multiple sensors. In recent years, one of the world's great challenges has been to improve energy efficiency.
Chapter Two : Literature Review
According to researchers, IoT and distributed computing have given massive progress to the smart home industry. In [54], researchers addressed a variety of home display (IHD) and automatic meter reading (AMR) systems in the context of providing energy management information. The authors provided a home robotic system for handling the home energy system in this study [63].
In this study [65], researchers presented a resource-based mobility-aware framework for energy management in a multi-occupancy intelligent house. This study [70] designed a methodology to intelligently manage energy consumption between consumer needs and energy saving based on sophisticated user intentions and automatic device control [71, 72].
Chapter Three : Proposed Methodology
Training Phase
- IoT Infrastructure
- Data acquisition layer
- Pre-processing layer
- Application layer
- Performance layer
- Fused machine learning empowered with fuzzy logic
In this layer, the output of the preprocessing layer will be the input of the performance layer. If the learning criteria are not met, the model must be retrained; the productivity is collected in the cloud database and sent to the fused machine learning approach. This layer is responsible for merging the predictions of the machine learning approach using a fuzzy inference system.
In this layer, the decision level fusion technique is intertwined with machine learning to achieve higher accuracy and better decision making. In this case, the steps are first detected individually in each sensor; then these individual decisions are combined and sent to predict energy utilization.
Validation Phase
If the learning criteria are not met, the model requires retraining of the data stored in the cloud to predict energy consumption. 48 Machine learning techniques (ANN and SVM) are applied in the prediction layer to monitor energy consumption. The output of the destruction layer will be provided to the performance layer, which will estimate the energy consumption smartness based on accuracy and miss rate and whether the learning conditions are met.
It clearly shows that the energy consumption prediction is bad if SVM is 0-50 and ANN is 0-50. It clearly shows that the energy consumption prediction is bad if SVM is 0-50 and ANN is 0-60.
Chapter four : Simulation Results
Table 2 shows that the proposed energy consumption prediction system in SVM training. Table 3 shows that the proposed energy consumption prediction system in SVM training. Table 4 shows that the proposed energy consumption prediction system in ANN training.
Similarly, 700 samples were taken, where negative indicates power consumption and positive indicates no power consumption. Table 5 shows that the proposed model of energy consumption in ANN training.
Chapter five : Conclusion
Pipattanasomporn, M., Kuzlu, M. and Rahman, S., "An Algorithm for Intelligent Home Energy Management and Demand Response Analysis," IEEE Trans. 34; Tabu Search for the Optimization of Household Energy Consumption." IEEE International Conference on Information Reuse and Integration, Waikoloa, HI, USA, September. 34; Demand Response Simulation Implementing Heuristic Optimization for Home Energy Management." North American Power Symposium (NAPS), Urbana-Champaign, USA, 26-28 September, 1-6
34;Coordinated Planning of Residential Distributed Energy Resources to Optimize Smart Home Energy Services." IEEE Transactions on Smart Grid. 34;Intelligent Energy Optimization for Understandable User Goals in Smart Home Environments." IEEE Transactions on Smart Grid.
