scholarly journals A Machine Learning and Internet of Things-Based Online Fault Diagnosis Method for Photovoltaic Arrays

2021 ◽  
Vol 13 (23) ◽  
pp. 13203
Author(s):  
Adel Mellit ◽  
Omar Herrak ◽  
Catalina Rus Casas ◽  
Alessandro Massi Pavan
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Good Accuracy ◽  
Raspberry Pi ◽  
Small Scale ◽  
Pv System ◽  
Pv Module ◽  
Photovoltaic Arrays ◽  
Diagnosis Method ◽  
The University

In this paper, a novel fault detection and classification method for photovoltaic (PV) arrays is introduced. The method has been developed using a dataset of voltage and current measurements (I–V curves) which were collected from a small-scale PV system at the RELab, the University of Jijel (Algeria). Two different machine learning-based algorithms have been used in order to detect and classify the faults. An Internet of Things-based application has been used in order to send data to the cloud, while the machine learning codes have been run on a Raspberry Pi 4. A webpage which shows the results and informs the user about the state of the PV array has also been developed. The results show the ability and the feasibility of the developed method, which detects and classifies a number of faults and anomalies (e.g., the accumulation of dust on the PV module surface, permanent shading, the disconnection of a PV module, and the presence of a short-circuited bypass diode in a PV module) with a pretty good accuracy (98% for detection and 96% classification).

scholarly journals Internet of Things Enabled Smart Microgrid

2020 ◽  
Vol 9 (6) ◽  
pp. 73-76
Keyword(s):  
Internet Of Things ◽  
Energy Distribution ◽  
Rural Areas ◽  
Urban Areas ◽  
Power Saving ◽  
Electrical Network ◽  
Raspberry Pi ◽  
Small Scale ◽  
Smart Power ◽  
Iot Platform

In the era of emerging technologies internet of things (IoT) and smart power grid, this two are major technology which would boost up the development of any country because of its perspective of smart and renewable technique. A microgrid is a small–scale localized energy grid with the capability to control the various electrical parameters and can be operated autonomously. This microgrid technology can be implemented in both rural and urban areas. In this paper, the author proposes the design of a smart microgrid system enabled IOT for a smart country. A microgrid is an excellent solution for providing a continuous supply of power during the failure of the main grid (blackouts issues). It can also be used in industries for providing additional power, and most importantly it can be implemented in areas like an island or remote/rural areas. The IoT can be a smart approach that offers the solution for detecting the fault and a convenient real-time technique to control and monitor the consumption of energy. In the present time, the non-renewable electrical network system is outdated due to the increasing demand for electrical energy. We have also proposed a smart energy distribution of power to the load by turning the appliances in power saving mode by the help of IoT platform. This smart microgrid can be installed in every house of the country for promoting smart energy distribution, a smart way of energy saving, and ecofriendly technique, this can connect parallel to the main grid supply. IoT will also help to monitor and detect the fault in a very faster manner. It will detect the fault in the microgrid with help of current sensor and voltage sensor installed in the transmission line. The current and voltage data collected from the sensors will be continuously sent to the microcontroller with the help of a Wi-Fi module and IoT platform. The microcontroller Raspberry pi 3 will store the data and it will continuously monitor the loads connected to the microgrid by the Cayenne’s IoT platform. The loads can be also triggered by the help of IoT platforms. The microgrid is incorporated with the net metering concept to make the power system reliable.

scholarly journals Vision based Office Automation and Security System using Machine Learning and Internet of Things

2018 ◽  
Vol 7 (2.24) ◽  
pp. 42
Author(s):  
Amber Goel ◽  
Apaar Khurana ◽  
Pranav Sehgal ◽  
K Suganthi
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Face Recognition ◽  
Internet Of Things ◽  
Office Automation ◽  
Detection Algorithm ◽  
Machine Learning Algorithms ◽  
Human Detection ◽  
Raspberry Pi ◽  
Efficient Tool

The paper focuses on two areas, automation and security. Raspberry Pi is the heart of the project and it is fuelled by Machine Learning Algorithms using Open CV and Internet of Things. Face recognition uses Linear Binary Pattern and if an unknown person uses their workstation, a message will be sent to the respective person with the photo of the person who uses the workstation. Face recognition is also being used for uploading attendance and switching ON and OFF appliances automatically. During un-official hours, A Human Detection algorithm is being used to detect the human presence. If an unknown person enters the office, a photo of the person will be taken and sent to the authorities. This technology is a combination of Computer Vision, Machine learning and Internet of things, that serves to be an efficient tool for both automation and security.  

scholarly journals SensIoT: An Extensible and General Internet of Things Monitoring Framework

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Marcel Großmann ◽  
Steffen Illig ◽  
Cornelius L. Matějka
Keyword(s):  
Internet Of Things ◽  
Performance Management ◽  
Current Data ◽  
Raspberry Pi ◽  
Service Orchestration ◽  
Monitoring Framework ◽  
Set Up ◽  
The University ◽  
Sensor Monitoring ◽  
Microservice Architecture

SensIoT is an open-source sensor monitoring framework for the Internet of Things, which utilizes proven technologies to enable easy deployment and maintenance while staying flexible and scalable. It closes the gap between highly specialized and, therefore, inflexible sensor monitoring solutions, which are only adjusted to a specific context, and the development of every other solution from scratch. Our framework fits a variety of use cases by providing an easy to set up, extensible, and affordable solution. The development is based on our former published framework MonTreAL, whose goal is to offer an environmental monitoring solution for libraries to guarantee cultural heritage to be conserved and prevented from serious damage, for example, from mold formation in closed stocks. It is a solution with virtualized microservices delivered by a famous container technology called Docker that is solely executable on one or more single board computers like the Raspberry Pi by providing automatic scaling and resilience of all sensor services. For SensIoT we extended the capability of MonTreAL to integrate commodity servers into the cluster to enhance the ease of setup and maintainability on already existing infrastructures. Therefore, we followed the paradigm to distribute microservices on small computing nodes first, thus not utilizing well-known cloud computing concepts. To achieve resilience and fault tolerance we also based our system on a microservice architecture, where the service orchestration is solved by Docker Swarm. As proof of concept, we are able to present our current data collection of the University of Bamberg’s Library that runs our system since autumn 2017. To make our system even better we are working on the integration of other sensor types and better performance management of SD-cards in Raspberry Pis.

scholarly journals Small-Scale Solar Photovoltaic Power Prediction for Residential Load in Saudi Arabia Using Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6759
Author(s):  
Mohamed Mohana ◽  
Abdelaziz Salah Saidi ◽  
Salem Alelyani ◽  
Mohammed J. Alshayeb ◽  
Suhail Basha ◽  
...  
Keyword(s):  
Machine Learning ◽  
Saudi Arabia ◽  
Regression Model ◽  
Prediction Models ◽  
Residential Buildings ◽  
Atmospheric Environment ◽  
Small Scale ◽  
Pv System ◽  
Power Sources ◽  
Pv Systems

Photovoltaic (PV) systems have become one of the most promising alternative energy sources, as they transform the sun’s energy into electricity. This can frequently be achieved without causing any potential harm to the environment. Although their usage in residential places and building sectors has notably increased, PV systems are regarded as unpredictable, changeable, and irregular power sources. This is because, in line with the system’s geographic region, the power output depends to a certain extent on the atmospheric environment, which can vary drastically. Therefore, artificial intelligence (AI)-based approaches are extensively employed to examine the effects of climate change on solar power. Then, the most optimal AI algorithm is used to predict the generated power. In this study, we used machine learning (ML)-based algorithms to predict the generated power of a PV system for residential buildings. Using a PV system, Pyranometers, and weather station data amassed from a station at King Khalid University, Abha (Saudi Arabia) with a residential setting, we conducted several experiments to evaluate the predictability of various well-known ML algorithms from the generated power. A backward feature-elimination technique was applied to find the most relevant set of features. Among all the ML prediction models used in the work, the deep-learning-based model provided the minimum errors with the minimum set of features (approximately seven features). When the feature set is greater than ten features, the polynomial regression model shows the best prediction, with minimal errors. Comparing all the prediction models, the highest errors were associated with the linear regression model. In general, it was observed that with a small number of features, the prediction models could minimize the generated power prediction’s mean squared error value to approximately 0.15.

scholarly journals Design of Automatic Watering and Humidity Systems Based on Internet of Things for Screenhouse

2021 ◽  
Vol 3 (2) ◽  
pp. 74
Author(s):  
Stefanus Marcellindo ◽  
Dina Mifika Sari ◽  
Savira Kharisma Putri ◽  
Muhammad Hammam Al-Choirie ◽  
Feri Adriyanto
Keyword(s):  
Machine Learning ◽  
Remote Control ◽  
Internet Of Things ◽  
Plant Growth ◽  
Solenoid Valve ◽  
Raspberry Pi ◽  
Good Growth ◽  
Electric Pump ◽  
Real Condition ◽  
Weather Changes

<p class="Abstract">Central Bureau of Statistics <span lang="EN-GB">reports that the population in Indonesia continues to increase every 10 years. The more the population swells, the greater the need for food. To meet the community's need for food availability, there are several agricultural obstacles such as growth criteria that are not by the type of commodity. Air temperature, humidity and intensity of water supply are included as criteria for plant growth (Budiman and Saraswati, 2005). Erratic weather changes make farmers unable to guarantee production results with good quality<sub>,</sub> and quantity. This research is designed using a tool that has been integrated with the </span>I<span lang="EN-GB">nternet of </span>T<span lang="EN-GB">hings system embedded in a raspberry pi as the overall processing core</span> [1]<span lang="EN-GB">. In addition to the raspberry pi, there are also several sensors as real condition readings, several relays as an electric pump controller and a solenoid valve as an electrical faucet controller, </span>and <span lang="EN-GB">these components will be connected</span><span lang="EN-GB">with the ESP8266/NodeMcu module using a wifi connection. The data read by these sensors is displayed on the smartphone screen as a monitoring function in real-time and also the user can directly take action using the remote control, both automatic actions using machine learning or manual action.</span><span lang="EN-US">This sophisticated screenhouse can perform irrigation actions and temperature regulation automatically without having to do direct monitoring every time. The use of this system in agriculture allows the owner to not need to do direct monitoring by coming to the screenhouse, users can simply monitor using a smartphone connected to the </span>I<span lang="EN-US">nternet network.</span><span lang="EN-US">The application of this system can carry out watering actions and regulate the temperature in the screenhouse room automatically according to the aspects of good growth criteria for plants. Like controlling the microclimate in the screenhouse. <em></em></span></p>

Solar powered infotainment spot: design, feasibility study and fabrication of an autonomous PV system

2015 ◽  
Vol 1771 ◽  
pp. 51-57 ◽  
Author(s):  
V. Weeda ◽  
O. Isabella ◽  
M. Zeman
Keyword(s):  
Energy Production ◽  
Ad Hoc ◽  
Small Scale ◽  
Pv System ◽  
System A ◽  
Pv Module ◽  
University Of Technology ◽  
Solar Powered ◽  
Shading Effects ◽  
Account Temperature

ABSTRACTFor increasing the awareness of photovoltaic (PV) conversion and its consumer developments, a PV-powered infotainment spot (information + entertainment) has been recently designed to be used in the campus of Delft University of Technology. This demonstrator provides information to people on campus via a rugged touchscreen that is powered only by solar energy. In this PV system, a 90-W rated flexible CIGS module was deployed as market alternative to rigid c-Si modules. A methodology to accurately estimate the irradiance on a curved plane was developed. Taking into account temperature, irradiance and shading effects, the energy production of the PV module was estimated to be 62 kWh/year. On the other hand, the load should exhibit an energy consumption of 19.3 kWh/year. By means of a 12-V DC mini grid, the excess energy was thus stored in a 120 Ah battery regulated via charge controller or made directly available with two ad-hoc USB ports for smartphone battery recharging. The realized prototype of the infotainment spot is potentially a completely autonomous small-scale PV system with zero loss of load probability.

scholarly journals Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

2021 ◽  
Vol 11 (6) ◽  
pp. 5066
Author(s):  
Vusi Sithole ◽  
Linda Marshall
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
The Internet ◽  
Use Case ◽  
Object Oriented Design ◽  
Learning Techniques ◽  
Knowledge Graphs ◽  
The Internet Of Things

<span lang="EN-US">Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.</span>

scholarly journals Artificial Intelligence System for Effective Detection of Animal Intervention in Croplands

2021 ◽  
Vol 9 (9) ◽  
pp. 518-523
Author(s):  
Abhay Patil
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Internet Of Things ◽  
Convolutional Neural Network ◽  
Detection Algorithm ◽  
Machine Learning Algorithms ◽  
Raspberry Pi ◽  
Single Shot ◽  
Intelligence System ◽  
Shot Detection

Abstract: Animal intervention is significant intimidation to the potency of the crops, which influences food security and decreases the value to the farmers. This suggested model displays the growth of the Internet of Things and Machine learning technique-based resolutions to surmount this obstacle. Raspberry Pi commands the machine algorithm, which is interfaced with the ESP8266 Wireless Fidelity module, Pi-Camera, Speaker/Buzzer, and LED. Machine learning algorithms similar to Regionbased Convolutional Neural Network and Single Shot Detection technology represents an essential function to identify the target in the pictures and classify the creatures. The experimentation exhibits that the Single Shot Detection algorithm exceeds than Region-based Convolutional Neural Network algorithm. Ultimately, the Twilio API interfaced software decimates the data to the farmers to take conclusive work in their farm territory. Keywords: Region-Based Convolutional Neural Network (R-CNN), Tensor Flow, Raspberry Pi, Internet of Things (IoT), Single Shot Detector (SSD)

scholarly journals Edge Machine Learning: Enabling Smart Internet of Things Applications

2018 ◽  
Vol 2 (3) ◽  
pp. 26 ◽  
Author(s):  
Mahmut Yazici ◽  
Shadi Basurra ◽  
Mohamed Gaber
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Power Consumption ◽  
High Performance ◽  
Machine Learning Algorithms ◽  
Raspberry Pi ◽  
Support Vector ◽  
Processing Power ◽  
Chip Technology ◽  
Speed Training

Machine learning has traditionally been solely performed on servers and high-performance machines. However, advances in chip technology have given us miniature libraries that fit in our pockets and mobile processors have vastly increased in capability narrowing the vast gap between the simple processors embedded in such things and their more complex cousins in personal computers. Thus, with the current advancement in these devices, in terms of processing power, energy storage and memory capacity, the opportunity has arisen to extract great value in having on-device machine learning for Internet of Things (IoT) devices. Implementing machine learning inference on edge devices has huge potential and is still in its early stages. However, it is already more powerful than most realise. In this paper, a step forward has been taken to understand the feasibility of running machine learning algorithms, both training and inference, on a Raspberry Pi, an embedded version of the Android operating system designed for IoT device development. Three different algorithms: Random Forests, Support Vector Machine (SVM) and Multi-Layer Perceptron, respectively, have been tested using ten diverse data sets on the Raspberry Pi to profile their performance in terms of speed (training and inference), accuracy, and power consumption. As a result of the conducted tests, the SVM algorithm proved to be slightly faster in inference and more efficient in power consumption, but the Random Forest algorithm exhibited the highest accuracy. In addition to the performance results, we will discuss their usability scenarios and the idea of implementing more complex and taxing algorithms such as Deep Learning on these small devices in more details.

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