Prediction of electricity consumtion based on complex computational method / Patrick Ozoh, Morufu Oyedunsi Olayiwola and Adepeju Abeke Adigun

• Main Authors: Ozoh, Patrick (Author), Olayiwola, Morufu Oyedunsi (Author), Adigun, Adepeju Abeke (Author)
• Format: Book
• Published: Universiti Teknologi MARA, Perak, 2022-05.
• Subjects: Article; PeerReviewed
• Online Access: Link Metadata
• Notes: https://ir.uitm.edu.my/id/eprint/61722/1/61722.pdf; Prediction of electricity consumtion based on complex computational method / Patrick Ozoh, Morufu Oyedunsi Olayiwola and Adepeju Abeke Adigun. (2022) Mathematical Sciences and Informatics Journal (MIJ) <https://ir.uitm.edu.my/view/publication/Mathematical_Sciences_and_Informatics_Journal_=28MIJ=29/>, 3 (1). pp. 56-65. ISSN 2735-0703
• Keywords: QA Mathematics; Neural networks (Computer science)

The tasks of finding and selecting an accurate computational method that exists to undertake individual characteristics with various computational methods were considered difficult and would take a long completing time. The main objective of this research is to conduct a thorough study involving techniques of various computational methods that normally used in modeling and forecasting real-world problems. This paper presents the comparison results of the computational modeling methods that tested on electricity consumption data of Sarawak Energy Malaysia. The three computational methods compared in this study were Box-Jenkins technique, regression method, and artificial neural network. The models were tested on data collected from Sarawak Energy in Malaysia with regard to electricity consumption by using MATLAB software. The verification of the three methods was done using the computational statistics measurement namely the root means square error and the mean absolute percentage error. The results show that the artificial neural network was the most outperformed technique in generating the accurate prediction.