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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Elia Erwani Hassan
Universiti Teknikal Malaysia Melaka
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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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.
Optimal location and sizing of battery energy storage system using grasshopper optimization algorithm Nur Syifa Nasyrah Razali; Zuhaila Mat Yasin; Nofri Yenita Dahlan; Siti Zaliha Mohammad Noor; Nurfadzilah Ahmad; Elia Erwani Hassan
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp647-654

Abstract

An energy storage system called a battery energy storage system (BESS) collects energy from various sources, builds up that energy, and then stores it in rechargeable batteries for future use. The battery's electrochemical energy can be discharged and supplied to buildings such as residences, electric cars, and commercial and industrial buildings. The advantages of utilizing BESSs, such as minimizing energy loss, improving voltage profile, peak shaving, and increasing power quality, may be reduced if incorrect decisions about the appropriate position and capacity for BESSs are chosen. Furthermore, the optimal position and size for BESSs are critical since deploying a BESS at every bus, particularly in an extensive network, is not a cost-effective option, and installing oversized BESSs would result in higher investment expenses. Hence, this study suggests a proficient method for identifying the most suitable position and the sizes of BESS to save costs. The grasshopper optimization algorithm (GOA) and evolutionary programming (EP) were employed to address the optimization challenge on the IEEE 69-bus distribution test system. The goal of the optimization is to minimize the overall cost. The findings indicate that the GOA has strong resilience and possesses a superior capacity for optimizing cost reduction in comparison to EP.
Appropriateness of EToU electricity tariff program for industrial type consumers: an investigation of cost benefit Mohamad Fani Sulaima; Farah Anishah Zaini; Amira Noor Farhanie Ali; Intan Azmira Wan Abdul Razak; Elia Erwani Hassan; Nur Elida Mohamad Zahari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 1: February 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i1.24260

Abstract

In mitigating the peak demand, the energy authority in Malaysia has introduced the enhanced time of use (EToU). However, the number of participants joining the programs is less than expected. Due to that reason, this study investigated the investment benefit in terms of electricity cost reduction when consumers subscribe to the EToU tariff scheme. The significant consumers from industrial tariff types have been focused on where the load profiles were collected from the incoming providers’ power stations. Meanwhile, ant colony optimization (ACO) and particle swarm optimization (PSO) are applied to optimize the load profiles reflecting EToU tariff prices. The proposed method had shown a reduction in electricity cost, and the most significant performance has been recorded congruently. For a maximum 30% load adjustment using ACO optimization, the electricity costs have been decreased by 10% (D type of tariff), 16% (E1 type of tariff), 9% (E2 kind of tariff), and 1.13% (E3 type of tariff) when compared to the existing conventional tariff. The cost-benefit of the EToU tariff switching has been identified where the simple payback period (SPP) is below one year for all the industrial types of consumers.
Co-Authors Aida Fazliana Abdul Kadir Amira Noor Farhanie Ali Arfah Ahmad Farah Anishah Zaini Intan Azmira Wan Abdul Razak Leong Lek Chung Mohamad Fani Sulaima Nazrulazhar Bahaman Nofri Yenita Dahlan Nur Elida Mohamad Zahari Nur Syifa Nasyrah Razali Nurfadzilah Ahmad Sharizad Saharani Siti Zaliha Mohammad Noor Zuhaila Mat Yasin Zul Hasrizal Bohari