A comparative study to obtain an adequate model in prediction of electricity requirements for a given future period

Ulkareem, Maysaa Abd; Awadh, Wid Akeel; Alasady, Ali Salah

doi:10.1109/iiceta.2018.8458079

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…The Minitab statistical software was used to simulate a regression analysis for the historical data which was similarly applied by (Ulkareem et al, 2018). The results obtained were reasonably close to that obtained when the PIM Model is used.…”

Section: Software and Simulationsupporting

confidence: 55%

Pseudo-Inverse Matrix Model for Estimating Long-Term Annual Peak Electricity Demand: The Covenant University’s Experience

Abdulkareem

Okoroafor

Awelewa

et al. 2019

IJEEP

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One of the major decision problems facing any electrical supply undertaking is the forecasting of peak power demand. A problem therefore arises when an estimate of future electricity demand is not known to prepare for impending possible increase in electricity demand. To overcome this problem, it is therefore imperative to evaluate the precise amount of energy required for a sustainable power supply to customers. In line with this goal, this study established a mathematical model of regression analysis using pseudo-inverse matrix (PIM) method for the assessment of the historical data of covenant University's electric energy consumption. This method predicts a more accurate and reliable future energy requirement for the community, with special consideration for the next one decade. The accuracy of prediction based on the use of PIM method is compared with the forecast result of the least squares model, commonly used by engineers in making long-term forecast. The error analysis result from the mean absolute percentage error and the root mean square error (RMSE) performed on the two models using mean absolute deviation shows that the PIM is the most accurate of the models. Though this method is examined using a University community, it can be further extended to cover the whole country, provided the historical data of the country's past electric energy consumptions is available.

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Section: Software and Simulationsupporting

confidence: 55%

Pseudo-Inverse Matrix Model for Estimating Long-Term Annual Peak Electricity Demand: The Covenant University’s Experience

Abdulkareem

Okoroafor

Awelewa

et al. 2019

IJEEP

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“…The ANN approach as a part of machine learning is used for classification and prediction [22]. The ANN has proved its accurate performance in many fields, such as electricity requirements [23], enhancement of electricity requirements [24], and solving routing problems in wireless environments [25].…”

Section: Introductionmentioning

confidence: 99%

Student’s Success Prediction Model Based on Artificial Neural Networks (ANN) and A Combination of Feature Selection Methods

Hamoud¹,

Humadi²

2019

Journal of Southwest Jiaotong University

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The improvements in educational data mining (EDM) and machine learning motivated the academic staff to implement educational models to predict the performance of students and find the factors that increase their success. EDM faced many approaches for classifying, analyzing and predicting a student’s academic performance. This paper presents a model of prediction based on an artificial neural network (ANN) by implementing feature selection (FS). A questionnaire is built to collect students’ answers using LimeSurvey and google forms. The questionnaire holds a combination of 61 questions that cover many fields such as sports, health, residence, academic activities, social and managerial information. 161 students participated in the survey from two departments (Computer Science Department and Computer Information Systems Department), college of Computer Science and Information Technology, University of Basra. The data set is combined from two sources applications and is pre-processed by removing the uncompleted answers to produce 151 answers used in the model. Apart from the model, the FS approach is implemented to find the top correlated questions that affect the final class (Grade). The aim of FS is to eliminate the unimportant questions and find those which are important, besides improving the accuracy of the model. A combination of Four FS methods (Info Gain, Correlation, SVM and PCA) are tested and the average rank of these algorithms is obtained to find the top 30 questions out of 61 questions of the questionnaire. Artificial Neural Network is implemented to predict the grade (Pass (P) or Failed (F)). The model performance is compared with three previous models to prove its optimality.

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“…By comparison, asymmetric encryption algorithms use two different keys, public and private. An encryption key is simply a binary data string used in the encryption [35]. The important matter is to keep the encryption key confidential when using the same one to encrypt and decrypt the information and to use keys that are difficult to guess.…”

Section: Advanced Encryption Standardmentioning

confidence: 99%

Hybrid information security system via combination of compression, cryptography, and image steganography

Awadh

Alasady

Hamoud

2022

IJECE

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<span lang="EN-US">Today, the world is experiencing a new paradigm characterized by dynamism and rapid change due to revolutions that have gone through information and digital communication technologies, this raised many security and capacity concerns about information security transmitted via the Internet network. Cryptography and steganography are two of the most extensively that are used to ensure information security. Those techniques alone are not suitable for high security of information, so in this paper, we proposed a new system was proposed of hiding information within the image to optimize security and capacity. This system provides a sequence of steps by compressing the secret image using discrete wavelet transform (DWT) algorithm, then using the advanced encryption standard (AES) algorithm for encryption compressed data. The least significant bit (LSB) technique has been applied to hide the encrypted data. The results show that the proposed system is able to optimize the stego-image quality (PSNR value of 47.8 dB) and structural similarity index (SSIM value of 0.92). In addition, the results of the experiment proved that the combination of techniques maintains stego-image quality by 68%, improves system performance by 44%, and increases the size of secret data compared to using each technique alone. This study may contribute to solving the problem of the security and capacity of information when sent over the internet.</span>

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A comparative study to obtain an adequate model in prediction of electricity requirements for a given future period

Cited by 5 publications

References 3 publications

Pseudo-Inverse Matrix Model for Estimating Long-Term Annual Peak Electricity Demand: The Covenant University’s Experience

Pseudo-Inverse Matrix Model for Estimating Long-Term Annual Peak Electricity Demand: The Covenant University’s Experience

Student’s Success Prediction Model Based on Artificial Neural Networks (ANN) and A Combination of Feature Selection Methods

Hybrid information security system via combination of compression, cryptography, and image steganography

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