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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Saurabh Gupta
Technocrats Institute of Technology and Science

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Soil pH periodic assortment with smart irrigation using aerial triboelectric nanogenerator Dhandapani Karthikeyan; Deeba Kannan; Brindha Gunasekaran; Hemalatha Selvaraj; Saurabh Gupta; Ravindran Ramkumar; Krishnasamy Vijayakumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1348-1358

Abstract

The paper presents an idea on pre-emptively ascertaining the soil pH value on an agriculture field amalgaming drone for aerial photo and subsequent smart irrigation model with the help of internet of things (IoT). The drone used for the aerial footage (multispectral imaging) consists of specialized cameras with filters that would help in ascertaining vegetation and health of the crops in the agriculture land. The IoT device used in smart irrigation model consists of sensors which accumulate data and execute the commands given in a recurring fashion of delay. Moreover, the use of triboelectric nanogenerator (TENG) would help in feasible energy harvesting for agricultural land use.
Long-term power prediction of photovoltaic panels based on meteorological parameters and support vector machine Saurabh Gupta; Palanisamy Ramasamy; Pandi Maharajan Murugamani; Selvakumar Kuppusamy; Selvabharathi Devadoss; Barath Suresh; Vignesh Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp687-695

Abstract

Solar energy is the most generally accessible energy in the entire globe. Proper solar panel maintenance is necessary to reduce reliance on imported energy. Continuous monitoring of the solar panel's power output is required. The deployment of internet of things (IoT) monitoring of solar panels for maintenance is the basis for the current research. A multi-variable long-term photovoltaic (PV) power production prediction approach based on support vector machine (SVM) is developed in this study with the aim of completely evaluating the influence of PV panels performance and actual operational state factors on the power generation efficiency. This study examines the use of SVM and climatic factors to forecast the long-term output of power from solar panels. A solar power facility in a semi-arid area provided the data utilized in this investigation. Temperature, humidity, wind speed, and sun radiation are some of the meteorological variables that were considered in the study. To anticipate the power generation of the panels, the SVM is trained using the climatic factors and the power generation data. The findings demonstrate that the SVM model consistently predicts the panels' long-term power generation with a high degree of accuracy.
Co-Authors Barath Suresh Brindha Gunasekaran Deeba Kannan Dhandapani Karthikeyan Hemalatha Selvaraj Krishnasamy Vijayakumar Palanisamy Ramasamy Pandi Maharajan Murugamani Ravindran Ramkumar Selvabharathi Devadoss Selvakumar Kuppusamy Vignesh Kumar