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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Elia Erwani Hassan
Universiti Teknikal Malaysia Melaka
Computer Science & IT Electrical & Electronics Engineering Engineering

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Smart irrigation system with photovoltaic supply Elia Erwani Hassan; Leong Lek Chung; Mohamad Fani Sulaima; Nazrulazhar Bahaman; Aida Fazliana Abdul Kadir
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3338

Abstract

Maximizing crop yielding is an extensive problem faced by the population in a country. The main issue comes from the farmer who still implemented the conventional method of irrigation that required human actions, especially for water pump operation. As an alternative, the automatic solution becomes a demand with the internet of things (IoT) support system to overcome the agriculture scenario. Meanwhile, multiple sensors controlled by the ESP32 microcontroller are also used to measure the crucial parameters that influenced the living conditions of crops and are called input parameters. Meanwhile, the implementation of a fuzzy logic controller is to control the timing of water volume based on the inputs data obtained through the sensors' responses. Solar energy is the main supply because of the zero-cost expense and environmentally friendly energy generation. In large, this research developed the smart irrigation system (SIS) with photovoltaic (PV) panels as a supply to sustain the energy required for empowering the entire process. As a result, the SIS is found as a successful system in controlling the best suitable time of water irrigation. The soil evaporation contents obtained from the experiment were also close to actual accurate data reference for Melaka state to verify the solution.
Coronavirus disease 2019; pandemic; Data analysis; Energy demand; Neural network; Self-organizing mapping; Mohamad Fani Sulaima; Sharizad Saharani; Arfah Ahmad; Elia Erwani Hassan; Zul Hasrizal Bohari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

The world faces a significant impact from the coronavirus disease 2019 (Covid-19) pandemic, which also influences energy consumption. This study investigates the substantial connection of the classified data between power consumption, cooling degree days, average temperature, and covid-19 cases information using mathematical and neural network approaches regression analysis, and self-organizing maps. It is well established that various data mining methods have revamped the classification process of data analytics. Specifically, this study investigates the correlation between the collected variables using regression analysis and selecting the best-matching unit under the normalization method using self-organizing maps. The selforganizing maps become better when the datasets have variations; the result denotes that this method produced high mapping quality based on the map size and normalization method. Furthermore, the data crossing connection is indicated using the regression analysis method. Finally, the classified data results during the movement control order are validated in self-organizing maps to achieve the study objective. By performing these methods, this study established that the correlation between the energy demand towards cooling degree days, average temperature, and covid-19 cases is very weak. The verification has been made where the ‘logistic’ normalization method has produced the best classification result.
Co-Authors Aida Fazliana Abdul Kadir Arfah Ahmad Leong Lek Chung Mohamad Fani Sulaima Nazrulazhar Bahaman Sharizad Saharani Zul Hasrizal Bohari