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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Nofri Yenita Dahlan
Universiti Teknologi MARA
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Development of option c measurement and verification model using hybrid artificial neural network-cross validation technique to quantify saving Wan Nazirah Wan Md Adnan; Nofri Yenita Dahlan; Ismail Musirin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (395.415 KB) | DOI: 10.11591/ijai.v9.i1.pp25-32

Abstract

This paper aims to develop a hybrid artificial neural network for Option C Measurement and Verification model to predict monthly building energy consumption. In this work, baseline energy model development using artificial neural networks embedded with artificial bee colony optimization and cross validation technique for a small dataset were considered. Artificial bee colony optimization with coefficient of correlation fitness function was used in optimizing the neural network training process and selecting the optimal values of initial weights and biases. Working days, class days and cooling degree days were used as input meanwhile monthly electricity consumption as an output of artificial neural network. The results indicated that this hybrid artificial neural network model provided better prediction results compared to the other model. The best model with the highest value of coefficient of correlation was selected as the baseline model hence is used to determine the saving. 
Harmonic Load Mitigation Using the Optimal Double Tuned Passive Filter Technique Muhammad Murtadha Othman; W Muhammad Faizol bin W Mustapha; Amirul Asyraf Mohd Kamaruzaman; Aainaa Mohd Arriffin; Ismail Musirin; Nur Ashida Salim; Zulkiffli Abdul Hamid; Nofri Yenita Dahlan
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 2: May 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i2.pp338-348

Abstract

Harmonic is one of the power quality disturbances customarily imminent in an unbalanced electrical system. Harmonic represents as the multiple integral of fundamental frequency of voltage and current inflicting towards the shifting in system frequency causing to a disruptive operation of electrical devices. This paper investigates on the performance of passive filter intrinsically by utilizing the inductor and capacitor electrical components to mitigate harmonic problem emanating from an unbalanced electrical system. In particular, explication in this paper will focus on the optimal parameters specification for the double tuned passive filter that used to overcome the phenomenon of harmonic issue. The two case studies constituting with different number of harmonic orders injected in a system were introduced to distinguish effectiveness of double tuned passive filter in solving the aforesaid problems. The parameters configuration of the passive filter are automatically tuned by the MATLAB® software to reduce the total harmonic distortion incurred in a system designed under the Simulink® software.
Modeling Baseline Energy Using Artificial Neural Network – A Small Dataset Approach Wan Nazirah Wan Md Adnan; Nofri Yenita Dahlan; Ismail Musirin
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp662-669

Abstract

In this work, baseline energy model development using Artificial Neural Network (ANN) with resampling techniques; Cross Validation (CV) and Bootstrap (BS) are presented. Resampling techniques are used to examine the ability of the ANN model to deal with a small dataset. Working days, class days and Cooling Degree Days (CDD) are used as ANN input meanwhile the ANN output is monthly electricity consumption. The coefficient of correlation (R) is used as performance function to evaluate the model accuracy. For this analysis, R is calculated for the entire data set (R_all) and separately for training set (R_train), validation set (R_valid) dan testing set (R_test). The closer R to 1, the higher similarities between targeted and predicted output. The total of two different models with several number of neurons are developed and compared. It can be concluded that all models are capable to train the network. Artificial Neural Network with Bootstrap Cross Validation technique (ANN-BSCV) outperforms Artificial Neural Network with Cross Validation technique (ANN-CV).  The 3-6-1 ANN-BSCV, with R_train = 0.95668, R_valid = 0.97553, R_test = 0.85726 and R_all = 0.94079 is selected as the baseline energy model to predict energy consumption for Option C IPMVP.
Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue Mashitah Mohd Hussain; Zuhaina Zakaria; Nofri Yenita Dahlan; Nur Iqtiyani Ilham; Zhafran Hussin; Noor Hasliza Abdul Rahman; Md Azwan Md Yasin
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 1: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i1.pp56-66

Abstract

This article aims to estimate the load profiling of electricity that provides information on the electrical load demand. In achieving this research implemented the neural network algorithm of joint approximate diagonalisation of eigen-matrices (JADE) to describe the load profile pattern for each point. Nowadays, utility providers claim that natural sources are used to generate power by rising consumer demands for energy. However, occasionally utility workers need to know the demand at certain location, corresponding to maintenance issues or for any shutdown area involved. A distribution pattern based on the data can be predicted based on the incoming data profile without having detailed information of certain load bus, the concept of derivatives was relevant to forecast the types of distribution data. The model was constructed with load profile information based on three different locations, and the concept of derivative was recognized, including the type of incoming data. Historical data were captured from a selected location in Malaysia that was proposed to train the JADE algorithm from three different empirical distributions of consumers, recording every 15 minutes per day. The results were analyzed based on the error measurement and compared with the real specific load distribution feeder information of needed profiles.
Development of Hybrid Artificial Neural Network for Quantifying Energy Saving using Measurement and Verification Wan n Nazirah Wan Md Adna; Nofri Yenita Dahlan; Ismail Musirin
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 1: October 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i1.pp137-145

Abstract

This paper presents a Hybrid Artificial Neural Network (HANN) for chiller system Measurement and Verification (M&V) model development. In this work, hybridization of Evolutionary Programming (EP) and Artificial Neural Network (ANN) are considered in modeling the baseline electrical energy consumption for a chiller system hence quantifying saving. EP with coefficient of correlation (R) objective function is used in optimizing the neural network training process and selecting the optimal values of ANN initial weights and biases. Three inputs that are affecting energy use of the chiller system are selected; 1) operating time, 2) refrigerant tonnage and 3) differential temperature. The output is hourly energy use of building air-conditioning system. The HANN model is simulated with 16 different structures and the results reveal that all HANN structures produce higher prediction performance with R is above 0.977. The best structure with the highest value of R is selected as the baseline model hence is used to determine the saving. The avoided energy calculated from this model is 132944.59 kWh that contributes to 1.38% of saving percentage.
Modelling base electricity tariff under the Malaysia incentive-based regulation framework using system dynamics Norlee Husnafeza Ahmad; Nofri Yenita Dahlan; Nor Erne Nazira Bazin; Yusrina Yusof; Arni Munira Markom
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1231-1240

Abstract

In the context of a single buyer (SB) electricity market, this study provides an electricity tariff model developed using system dynamics (SD). Using data from the Malaysian electricity supply industry (MESI), the model was developed with the intent of evaluating the influence of load variation on Malaysia’s base electricity tariff. Given that Malaysia’s electricity demand has increased significantly over the past few years in unison with the country’s economic growth and modernization, this model is developed to investigate the relationship between the two. Moreover, the lack of a comprehensive MESI upstream market model that can monitor this issue was the impetus for this research. This study employed an SD approach, as it is a well-known technique for simulating complex systems and analyzing the existing dynamism between each variable and each system. This model can be a valuable tool for developing an electrical tariff model. Findings revealed that the base electricity pricing on the MESI upstream market is affected by load growth variation during the 30-year time. Since new power sources are needed to meet demand, the tariff becomes more expensive as the load increases. This model may benefit the utility or generating company plan for future generation.
Hybrid load forecasting considering energy efficiency and renewable energy using neural network Adriana Haziqah Mohd Aizam; Nofri Yenita Dahlan; Saidatul Habsah Asman; Siti Hajar Yusoff
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp759-768

Abstract

In recent years, the relationship between a country's gross domestic product (GDP) and its electricity consumption has changed significantly due to increased energy efficiency (EE) and renewable energy (RE) adoption. This decoupling disrupts conventional load forecasting models, affecting utility companies. This study has developed an innovative solution using an artificial neural network (ANN) Hybrid method for load forecasting, resulting in a remarkably accurate model with 99.68% precision. Applying this model to Malaysia's electricity consumption from 2020 to 2040 reveals a significant 13% reduction when accounting for EE and RE trends. This method aids risk management, contingency planning, and decision-making by accurately reflecting changing energy usage dynamics influenced by EE and RE sources.
Modelling soil deposition predictions on solar photovoltaic panels using ANN under Malaysia’s meteorological condition Muhammad Aiman Amin Muhammad Suhaimi; Nofri Yenita Dahlan; Saidatul Habsah Asman; Natarajan Rajasekar; Hassan Mohamed
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp796-805

Abstract

Solar photovoltaic (PV) panels performance is influenced by various external factors such as precipitation, wind angle, ambient temperature, wind speed, transient irradiation, and soil deposition. Soiling accumulation on panels poses a significant challenge to PV power generation. This paper presents the development of an artificial neural network (ANN)-based soil deposition prediction model for PV systems. Conducted at a Malaysian solar farm over three months, the research utilized power output data from the inverter as model output and meteorological data as input variables. The model employed the Levenberg-Marquardt backpropagation method with Tansig and Purline activation functions. Performance assessment via statistical comparison of experimental and simulated results revealed a coefficient of determination (R2) value of 0.68073 for the ANN architecture of 5 input layers, 30 hidden layers, and 1 output layer (5-30-1). Sensitivity analysis highlighted relative humidity and wind direction as the most influential parameters affecting PV soiling rate. The developed ANN model, combined with sensitivity analysis, serves as a robust foundation for enhancing the efficiency of smart sensors in PV module cleaning systems.
Evaluation of the time-of-use tariff responsiveness for plug-in electric vehicle home charging in Malaysia Nurliyana Baharin; Mohamad Fani Sulaima; Nofri Yenita Dahlan; Hazlie Mokhlis
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp769-776

Abstract

Plug-in electric vehicles (PEVS) have become increasingly popular as a viable transportation option as owners can charge them at home. This will add much energy to the house if the users charge their PEVs at home. The PEV charging load will lead to extra energy demand on the distribution network, and the users will need to pay more for electricity if they use the current domestic tariff in Malaysia. This research aims to analyze the PEV charging costs using time-of-use (ToU) tariffs with different time segmentations and price elasticity. The effect of four residential load profile patterns has also been investigated in Malaysia as a case study. Four PEV charging scenarios were created, and the charging times were set according to Malaysian driving styles, with charging times starting at 6 PM, 10 PM, and 9 AM. The PEV and electric vehicle supply equipment (EVSE) are set to be homogeneous, and the EV was assumed to have a minimum state-of-charge of 20%. The main contribution of this paper is the selection of the ToU tariff segmentation, where the structure of the smallest time segmentation gave the lowest electricity bill per month compared to the Tenaga Malaysia Berhad (TNB) domestic tariff.
An innovative fast iterative process algorithm computerization for intermittency LSSPV generation reconfiguration Mashitah Mohd Hussain; Zuhaina Zakaria; Nofri Yenita Dahlan; Ihsan Mohd Yassin; Mohd Najib Mohd Hussain
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.pp628-638

Abstract

The recent implementation of solar photovoltaic (SPV) power generation in low-voltage distribution networks has increased due to its environmentally friendly technology, low cost, and high efficiency. However, SPV generation carried both the availability of uncertainty and intermittency on power energy exceeding voltage range, increased losses during reverse power flow action, and energy transmission problems. This paper presents a new capabilities methodology with accurate analysis to simulate the intermittent nature of SPV energy including normal generators associated with uncertain customer demand of high resolution with 1-minute temporal resolution using a fast iterative process algorithm (FIPA) simulated by Python programming. The primary goal is to address the unpredictable nature of SPV using computer operation technology connected to a real network with a fast iteration process. The result shows that in 0-10% of standard generators, grid energy (GE) is still required in daily supply, and the intermittent nature influences voltage violations and losses. Besides, the prediction typical SPV method (zero fluctuation) can serve as guidelines for engineers to design the photovoltaic (PV) module reducing its fluctuating nature and battery installation area. The research provides utilities with accurate information to plan for various difficulties at different levels of PV penetration while reducing time, effort, and resource utilization.
Co-Authors Aainaa Mohd Arriffin Adriana Haziqah Mohd Aizam Amirul Asyraf Mohd Kamaruzaman Arni Munira Markom Elia Erwani Hassan Hassan Mohamed Hazlie Mokhlis Ihsan Mohd Yassin Ismail Musirin Mashitah Mohd Hussain Md Azwan Md Yasin Mohamad Fani Sulaima Mohd Najib Mohd Hussain Muhammad Aiman Amin Muhammad Suhaimi Muhammad Murtadha Othman Natarajan Rajasekar Noor Hasliza Abdul Rahman Nor Erne Nazira Bazin Norlee Husnafeza Ahmad Nur Ashida Salim Nur Iqtiyani Ilham Nur Syifa Nasyrah Razali Nurfadzilah Ahmad Nurliyana Baharin Saidatul Habsah Asman Siti Hajar Yusoff Siti Zaliha Mohammad Noor W Muhammad Faizol bin W Mustapha Wan n Nazirah Wan Md Adna Wan Nazirah Wan Md Adnan Wan Nazirah Wan Md Adnan Yusrina Yusof Zhafran Hussin Zuhaila Mat Yasin Zuhaina Zakaria Zulkiffli Abdul Hamid