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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Nofri Yenita Dahlan
Universiti Teknologi MARA
Computer Science & IT Electrical & Electronics Engineering Engineering

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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.
Co-Authors Aainaa Mohd Arriffin Amirul Asyraf Mohd Kamaruzaman Arni Munira Markom Ismail Musirin Mashitah Mohd Hussain Md Azwan Md Yasin Muhammad Murtadha Othman Noor Hasliza Abdul Rahman Nor Erne Nazira Bazin Norlee Husnafeza Ahmad Nur Ashida Salim Nur Iqtiyani Ilham 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 Zuhaina Zakaria Zulkiffli Abdul Hamid