Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things (IoT). The IoT is the backbone of smart city applications such as smart grids and green energy management. In smart cities, the IoT devices are used for linking power, price, energy, and demand information for smart homes and home energy management (HEM) in the smart grids. In complex smart grid-connected systems, power scheduling and secure dispatch of information are the main research challenge. These challenges can be resolved through various machine learning techniques and data analytics. In this paper, we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement, for the smart grid. The proposed collaborative execute-before-after dependency-based requirement algorithm works in two phases, analysis and assessment of the requirements of end-users and power distribution companies. In the first phases, a fixed load is adjusted over a period of 24 h, and in the second phase, a randomly produced population load for 90 days is evaluated using particle swarm optimization. The simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction, peak to average ratio, and power variance mean ratio than particle swarm optimization and inclined block rate.
The Internet of Things (IoT) and drones have enabled various smart city applications. Drones are dynamic flying nodes whose connectivity is established by intelligent IoT gadgets. The use of smart drones can improve energy utilization, information compilation, security and privacy, disaster management, living standards, and public safety [
Machine learning techniques, including Q-learning and reinforcement learning, have provable strategies and are considered the best solutions. Fully automatic machines are installed, such as in safety, shipping, water control, and self-instructed smart bins that learn from the environment and reduce energy costs. Optimization is another new paradigm that can be used to reduce electricity bills. Renewable power sources like energy storage systems, wind, and photovoltaic panels can reduce costs. The energy management system is used for renewable energy in smart grids. Some home appliances are used in this scenario to examine loads that are managed by the energy management system (EMS). Three case studies are evaluated, considering parameters such as time-of-use and pricing schemes, where the simulation results clearly show the efficiency of the EMS [
The advancements in home energy management systems (HEMSs) have transformed life in the digital world. This paper describes HEMS architecture and functionalities like monitoring, logging, control, management, and alarms, which results in balanced energy prices. We briefly explain renewable energy management, which alters peak load utilization to save power [
The CDBR technique is used in a smart grid to reduce electricity pricing for automatic appliances.
The proposed scheme is evaluated in terms of convergence time, peak-to-average ratio, power utilization, and ratio of mean value.
Electricity cost is calculated for 90 days using MATLAB-based simulations, and CDBR is compared to benchmark algorithms with and without optimization.
The rest of this article is structured as follows. Section 2 reviews related work. Section 3 introduces the proposed CBDR algorithm for smart cities. Results are discussed in Section 4. Section 5 relates our conclusions and discusses future research directions.
The idea of solar energy management was introduced in the early 1980s [
In smart grid architecture, automated metering infrastructure was implemented between electric power utilities and end users [
Machine learning in smart homes uses datasets as input to forecast output values. Smart homes use machine learning and sensors to gather information from nodes that can be utilized to find broken links or sensors. The system learns and improves from experience to make better decisions. Residential energy management systems use automatic switches. Switching decisions are based on artificial neural networks (ANNs) and support vector machines (SVMs) that will efficiently switch the load to local energy storage called renewable energized systems, which results in reduced power utilization in the power grid. A simulation analysis using SVM shows better results in terms of convergence time, peak-to-average ratio, power utilization, and ratio of mean value compared to artificial neural networks [
The integration of various devices and applications leads to the concept of smart homes, and new dynamics of this area will cover security and data privacy. Some key long-term goals include secrecy, authenticity, availability, and authorization [
Smart homes include automatically operating devices like air conditioners, washing machines, fans, televisions, water pumps, solar panels, and windmills. A real-time pricing (RTP) model is used that charges consumers on a time-slot basis. One hour is divided into six slots of 10 min, which makes 144 time slots in a day. The proposed algorithm is used to schedule power in smart homes, showing a tendency to reduce and smooth high load peaks to attain a preferable peak-to-average ratio. The proposed technique works in two phases. In the first stage, a fixed load is adjusted over a period of 24 h, and the second stage is evaluated using a rand function to randomly produce a population load for 90 days using PSO. The technique is repeatedly used to obtain simulation results for CDBR optimization, which results in a reduction in the peak-to-average ratio. In the proposed scenario, a house can have a minimum of eight appliances, and a maximum of 16. Appliances may run 24 h on a daily basis, i.e., continuous usage. The proposed collaborative execute-before-after dependency-based requirement is shown in
Providing the optimal solution in a smart grid, the PSO plays a vital role. However, to get a better decision support system, CDBR is the best strategy by which to secure the communication channel. CBDR incorporates PSO to get better results, while the disagreement in a priority value pair is calculated using
Here,
Similarly the disagreement between Pi and Si is calculated using
The current solution represents the prime concern list, which is the basic requirement for available iterations in the sequence using the position vector. V is the particle velocity, and
The electricity prices using IBR are better because the price of electricity is computed based on the actual usage and real-time estimation. The RTP-IBR electricity pricing scheme for every time slot is different. Some end users know when electricity prices will be low, and try to make use of home appliances during those periods. Many people use electricity simultaneously, thinking that this is the low-price time based on IBR estimation. This results in a high load. Consequently, prices are driven higher before IBR can signal this to consumers/users.
A description of the simulation results is presented in the form of graphs and tables to verify the performance of the proposed scheme compared to traditional evolutionary algorithms with IBR. Various metrics are used, such as power usage patterns, cost of electricity, and average-to-mean ratio.
We used MATLAB for simulation to evaluate computational techniques such as PSO and CDBR. The group size, i.e., the number of houses, was about 100, with 16 subcomponents. The results shown are the average of many simulations. We ran simulations 50 times and present the average values here.
We trained our proposed CDBR techniques using the data of four months, from May 27, 2018, to August 24, 2018, from a U.S.-based company named Ameren. The power usage pattern for 45 days is shown in
The proposed CDBR in combination with IBR gives better results than PSO in conjunction with IBR. The mean values are tabulated in
Algorithm | Mean value |
---|---|
W/O optimization | 810.63 |
With PSO and IBR | 508.42 |
With CDBR and IBR | 376.96 |
Algorithm | Mean value |
---|---|
W/O optimization | 3.295 |
With PSO and IBR | 2.689 |
With CDBR and IBR | 2.429 |
The simulation results of the proposed CDBR scheme show a reduction compared to PSO. CDBR gives an average improvement of 0.26 in peak-to-average ratio (PAR), and without optimization it gives an improved average of 0.866.
Algorithm | Mean value | |
---|---|---|
W/O optimization | 1 | |
With PSO and IBR | 0.4124 | |
With CDBR and IBR | 0.2132 |
The peak-to-average ratio of power usage is better when using CDBR than with PSO. It also increases the number of users, as shown in
Technique | Reduction of cost in % | Peak-to-average ratio | Average ratio of mean value using power usage |
---|---|---|---|
PSO | 37.28088 | 18.3915 | 0.5876 |
CDBR | 53.4979 | 26.28225 | 0.7868 |
Smart cities are a promising area of research. Smart cities must provide cost-effective and efficient solutions to make humans comfortable. One such solution is energy management, i.e., a smart grid. It is achieved by knowing the demand-to-supply ratio in a locality using machine learning algorithms. To balance demand and response at the power generation side, we have proposed a CDBR algorithm based on particle swarm optimization. The algorithm helps to maintain a smooth power usage pattern and reduce the peak-to-average ratio. The algorithm shows significant improvements in simulation-based results in terms of fast convergence. In the future, CDBR can be used with a cluster community home energy management system. The entities in a smart home and smart grid are interconnected using IoT. Wireless communication among these devices is an easy target of intruders. Securing the smart grid from cyber-attacks is mandatory, and warrants future work.