IoT-based Energy Management System Research Articles

An IoT-Enabled Smart Energy Management System to Improve Energy Efficiency in University Laboratory

Efficient energy management remains a challenge in university laboratories. The aim of this study is to develop and deploy an Internet of Things (IoT) system that can automatically adjust electronic devices to improve the efficiency of power consumption in the university laboratory. An advanced system is urgently needed to support sustainable and effective energy management. A smart IoT-based energy management system can improve energy efficiency, reduce operating costs, and reduce negative environmental impacts. The system enables both human and automated control of lighting and air conditioning using scheduling and occupancy detection. This development-based research uses a product design methodology that includes both software and hardware. The software product in this research is an IoT platform website for managing IoT devices. The essential processes include defining project goals and requirements, designing a system architecture, creating a user experience design, developing and integrating components, and conducting tests. The system is successful in automatically monitoring and controlling electronic devices based on certain parameters such as scheduling and presence detection. The system meets most of the specified functional and non-functional requirements as demonstrated in experiments, although it is somewhat limited by hardware limitations. Ultimately, the system increases the energy efficiency of the laboratory and thus successfully fulfills the research goal. This innovative project could be a blueprint for other smart energy control efforts.

Implementation of a Real-Time IoT Based Energy Management System

Energy Management System has received a lot of attention from both academia and industries due to the rate of energy consumed by electrical appliances. These industries are faced with inflation in the cost of electrical bills. Therefore, this study presents the design and implementation of a real-time IoT based Energy Management System (EMS) to reduce the inflation in the cost of these electrical bills. The system comprises four (4) basic units namely; the sensing unit, control unit, display unit and switching unit. The sensing unit comprises of two sensors namely; Current Transformer (CT) sensor and voltage sensor which senses and measures the parameters (voltage and current) and sends the parameters to the control unit. This control unit comprises two (2) microcontrollers namely; Arduino Nano and ESP32. The Arduino Nano processes the received data from the sensing unit and sends the processed data to the display unit and ESP32 microcontroller for real-time monitoring. In addition, the ESP32 microcontroller sends the processed data to the cloud. Furthermore, the web-based application receives the measured parameters from the cloud and thus, allowing the user to monitor as well as control the rate of energy consumption of electrical appliances. The experimental results obtained showed that the developed system efficiently monitors and control the rate of energy consumption of the three applied loads (Edison 2.7 bulb, standing fan and phone charger). Hence, reduces the cost of electrical bills consumed by these appliances.

Design and implementation of an energy management system based on distributed IoT

A Distributed IoT-based energy management system is planned and executed in this article. We tested the suggested strategy at Duhok and Shekhan, two institutes far apart. Utilizing ESP32 microcontrollers, the suggested system features two main phases (Monitoring and Controlling). There has been carefully monitoring and regulation of all power usage indicators, including Voltage, current, and Frequency. Three primary approaches are taken with each institution: monitoring all metrics for the whole institution, monitoring all metrics for the uncontrolled Lab, and monitoring and regulating all metrics for the controlled Lab. This technology gathers data incessantly, resulting in a large database for energy management. Up to this point, we have collected over two million data rows, and by the end of 2023, we anticipate collecting over ten million. Server-side protocols Message Query Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP) were used to collect the implementation results. According to the implemented findings, one can see that the energy consumption reduction ratios for the Duhok-controlled laboratory were (Weekly = 1.94%, Monthly = 21.65%, and Year-to-Date = 26.38%). There are several critical values recorded during the system's implementation at Shekhan Institute: maximum Voltage (281.2 V), Maximum Frequency (86.3 Hz), and minimum Power-Factor (0.07).

Data transmission using secure hybrid techniques for smart energy metering devices

Data transfer between components, continuous device monitoring, and other features are possible with an IoT-based energy management system. Due to its lightweight, compact, and expandable characteristics, the Message Queuing Telemetry Transport protocol (MQTT) has recently played an important role in IoT. Since the MQTT protocol cannot provide security between the publishing house and the subscription service, cryptographic techniques can be enforced. This work implements a public-key encryption system-based Elliptic Curve Cryptography (ECC) technique to secure data from Publisher's end to the Subscriber's end. The data from the Raspberry plane is sent to the MQTT publisher with elliptical curve encryption built-in feature. The message/text is converted into cipher text and conveyed through the topic within the MQTT protocol to the subscribed Broker. The subscriber decrypts the cipher text using the elliptical curve cryptography technique with the secret key. This protects data, personal privacy, and safety in a more efficient and convenient way. ECC and MQTT protocols implemented in this work proved to be worthy for both burst mode and continuous data transmissions when compared to the conventional cryptographic techniques.

An IoT-based energy management system for AC microgrids with grid and security constraints

This paper proposes an Internet-of-Things (IoT) based energy management system (EMS) for the optimal operation of unbalanced three-phase AC microgrids. The system utilizes a software architecture based on microservices, which includes a stochastic economic dispatch optimizer (EDO), a database, a web-based graphical user interface (GUI), and an application programming interface (API). The EDO uses a mixed-integer linear programming (MILP) model to ensure the day-ahead dispatch of the distributed energy resources (DERs) in the microgrid while adhering to grid constraints such as voltage, current, and power limits. Additionally, the optimization module takes into account security constraints for unplanned islanded operation, as well as stochastic scenarios of local demand and renewable generation. To assess the performance of the proposed IoT-based EMS, tests are conducted using a real-time simulator in a software-in-the-loop (SIL) experimental setup. Actual data from a microgrid located at the State University of Campinas (UNICAMP) in Brazil is utilized for the tests. The microgrid consisted of a photovoltaic (PV) system, a battery energy storage system (BESS), a thermal generation unit, and variable demands. Results indicated the effectiveness of the proposed IoT-based EMS in monitoring the operation of the microgrid and defining the optimal day-ahead dispatch of local DERs.

Development of an Iot Based Energy Management System for Air Handling Unit

The emergence of automation and smart power management systems pave to create a better energy management system. Measuring energy savings and potential reduction of peak demand in a building due to adjustment of AHU (Air Handling Unit) setpoint during working hours is critical for ideal demand-side allocations. Since Air Handling Units are one of the major energy consuming system in a building. This has been a great challenge given too many variable physical characteristics and AHU system thermal dynamics in commercial buildings. Based on the building model, developed and authenticate against measured power consumption data from a smart energy meter, this project deals with how energy savings and peak demand reduction potentials of a building are impacted by AHU, setpoint changes during a working period. It is of the utmost necessity to make a prototype for an existing AHU in order to minimize wastage of power in older buildings without any major renovation. This can be achieved by making the existing system into a closed loop system by our model prototype.

IOT based Energy Management System for an Autonomous Hybrid Micro-Grid

Raising rate and require of power has led a lot of organizations to discover elegant ways for monitoring, controlling and reduction energy. To create an innovative idea to reduce the rate of energy consumption smart EMS (Energy Management System) is proposed in this paper. To develop IoT technologies and Big Data is used to improved hold energy utilization in commercial, housing and industrial sectors. An EMS is used to build smart homes is proposed for he developed cities. In this system, every residence tool is interfaced with a data attainment module that is an IoT object with an exclusive IP address ensuing in a huge mesh wireless network of devices. The data gaining SoC (System on Chip) module collects energy utilization data from every device of every stylish residence and send data to a centralized server for supplementary handing out and study. This information from all housing areas accumulates in the utility’s server as Big Data. EMS consumes off-the-shelf BI (Business Intelligence) and Big Data surveys software packages which improves the energy usages also to assemble user order. While air conditioning gives to 60% of power use in American countries, HVAC (Ventilation, Air Conditioning and Heating) are in use as a research to approve the proposed system.

IOT based Energy Management System for an Autonomous Hybrid Micro-Grid

Raising rate and require of power has led a lot of organizations to discover elegant ways for monitoring, controlling and reduction energy. To create an innovative idea to reduce the rate of energy consumption smart EMS (Energy Management System) is proposed in this paper. To develop IoT technologies and Big Data is used to improved hold energy utilization in commercial, housing and industrial sectors. An EMS is used to build smart homes is proposed for he developed cities. In this system, every residence tool is interfaced with a data attainment module that is an IoT object with an exclusive IP address ensuing in a huge mesh wireless network of devices. The data gaining SoC (System on Chip) module collects energy utilization data from every device of every stylish residence and send data to a centralized server for supplementary handing out and study. This information from all housing areas accumulates in the utility’s server as Big Data. EMS consumes off-the-shelf BI (Business Intelligence) and Big Data surveys software packages which improves the energy usages also to assemble user order. While air conditioning gives to 60% of power use in American countries, HVAC (Ventilation, Air Conditioning and Heating) are in use as a research to approve the proposed system.

Intelligent Edge Computing for IoT-Based Energy Management in Smart Cities

In recent years, green energy management systems (smart grid, smart buildings, and so on) have received huge research and industrial attention with the explosive development of smart cities. By introducing Internet of Things (IoT) technology, smart cities are able to achieve exquisite energy management by ubiquitous monitoring and reliable communications. However, long-term energy efficiency has become an important issue when using an IoT-based network structure. In this article, we focus on designing an IoT-based energy management system based on edge computing infrastructure with deep reinforcement learning. First, an overview of IoT-based energy management in smart cities is described. Then the framework and software model of an IoT-based system with edge computing are proposed. After that, we present an efficient energy scheduling scheme with deep reinforcement learning for the proposed framework. Finally, we illustrate the effectiveness of the proposed scheme.

IoT-Based Energy Management System with Data Logging Capability

IoT-Based Energy Management System with Data Logging Capability