MONITORING SYSTEM

Face detection and recognition are computer vision techniques used to identify and locate human faces within digital images or video frames, and to subsequently analyze and verify the identity of individuals based on their unique facial features. Some of the algorithms used for implementing face detection and recognition are Local Binary Pattern, Histogram of oriented Gradient, Linear discriminant analysis, and convolutional neural networks with classifier such as Support Vector machine, e.t.c. Face detection and recognition system has become relevant in security and access control, automated Attendance tracking, user authentication, Biometric identification, Human Computer Interaction and many other areas. This project is implemented using python programming language, because python programming language allows programmers flexibility, therefore is of no threat to write- ability, readability and reliability, it has it libraries for the implementation of the project. And the test run of the project result is contained in Appendix D

This project focuses on designing a monitoring system using an Internet of Things (IoT) device that sends data to Thingspeak, a cloud-based platform. Solar system are affected by various factors such as temperature, weather condition and light intensity, on their output performance. These factors can lead to inefficient performance, increased maintenance cost and so on. Therefore, the aim of the project is to design and implement an intelligent virtual monitoring system that utilizes IoT to monitor PV solar panel array. To achieve this work, the role was centred on light sensor, voltage sensor and temperature sensor. These sensors are connected to on IoT gateway or a local data acquisition unit that acts as a bridge between the sensors and the internet collects the sensors data and prepares it for transmission to the cloud. Testing the monitoring system works satisfactorily. Having humidity of 85g/m3 on average, voltage of 35.78V during the day and 0V at night, temperature as high as 30 0c and low as 27 0c and luminance of 4201cd/m2 during the day and 0cd/m2 at night

Poor power management and monitoring have resulted in technical and non-technical electrical losses in the power distribution system, ranging from distribution line cuts to electricity theft and non-payment of electricity bills. As a result, stakeholders' reciprocal relationship and accountability have been severed in developing countries such as Nigeria. Therefore, the design and evaluation of an Automated Metering Power Management and Monitoring System (AMPMS) is critical in order to effectively manage, monitor and reduce the cost of power usage. A well-structured questionnaire was developed and administered to selected members of the public in Edo state of Nigeria to investigate the effectiveness of the existing metering system as well as load consumption. The collected data was then analyzed using SPSS, and Microsoft Excel 2016. This was followed by the simulation and design of an automated open-circuit fault detection and reporting system on the service lines of the power distribution network using a MATLAB Appdesigner as a tool. Thereafter, the design of an automated digital energy meter for power management and monitoring system was undertaken. The meter utilized a Hall effect current sensor, voltage sensor, Wi-Fi module, and microcontroller. A cost-effective consumption planner was also developed based on a mobile software application for metering status, monitoring, load management, and a web-based system. The monitoring base station was interfaced with a long-range (LoRa) wireless transceiver to facilitate communication between the digital energy meters and the base station. The software was developed to generate customers’ bills and reports, live comparison of energy usage on a centralized wireless-based system, tampering of meters, and faults on the three phases (Red, Yellow, and Blue) were detected, monitored, and shut down from the monitoring station. By analyzing the energy value of 2.751 kWh, power consumption of 121.4W, current, and voltage associated with consumer usage, the utility company would be able to accurately determine load consumption using the AMPMS monitoring web-based system. To evaluate the system, it was tested with various datasets representing the energy and power consumption of different electrical appliances. The test results confirmed that the system could precisely measure power usage and provide users with real-time feedback. This was demonstrated by comparing the energy consumed by an automated meter (3.02 kWh) and an existing metering system (4.442 kWh) while measuring twelve loads over a four-hour period. Consequently, automated meters consume less energy compared to the existing meters. Furthermore, the system's effectiveness in reducing power consumption costs was assessed by comparing the power and energy consumption of households using the system versus those not using it. The findings revealed a significant reduction in power consumption for households that utilized the system. Additionally, the system had the capability to alert users if an appliance was consuming excessive power, empowering them to turn it off and further decrease consumption costs. This system reduces the possibility of non-payment of electricity bills, meter tampering, labour costs, and electricity theft while also improving meter reading accuracy, awareness of electricity consumption, power monitoring, management, and economic viability.