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Pasangkunan, Raya
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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APPLICATION OF ARTIFICIAL NEURAL NETWORK (ANN) METHOD TO FORECAST LONG-TERM ELECTRICITY LOAD (CASE STUDY: KARANRANG ISLAND) Pasangkunan, Raya
Journal of Electrical Engineering and Informatics Vol. 2 No. 2 (2025): Journal of Electrical Engineering and Informatics
Publisher : Fakultas Teknik Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jeeni.v2i2.7418

Abstract

In the islands, the use of electrical energy is not only for household purposes, but also to support the education, health, small industry and transport sectors. This increase in electrical energy demand is increasingly urgent, given the importance of electricity in driving technology, improving people's quality of life, and supporting the sustainability of life, thus requiring special attention to maintain the availability of electrical energy. Grid management involves proper planning of load requirements. Therefore, accurate load forecasting will ensure the safe, reliable, and economical operation of power systems. In recent years, researchers have proposed many models for electricity forecasting. In this study, the backpropagation ANN method is used to forecast electrical energy demand until 2035 on Karanrang island which will be compared with the prediction results using the linear regression method. Based on the results of research and discussion of the prediction results, it can be concluded that the MAPE value of the backpropagation ANN is 8% and from the linear regression method is 10.4% so it can be concluded that prediction with the backpropagation ANN method is more optimal. The MAPE value of 8% is categorised as a very good prediction result.
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