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All Journal Journal of Computer Networks, Architecture and High Performance Computing Jurnal IPTEK Bagi Masyarakat
Rambe, Feby Mayori
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Mechanical Engineering Physics Public Health Social Sciences

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SHORT-TERM ELECTRICITY LOAD FORECASTING SEASONAL PATTERN USING TIME SERIES REGRESSION (TSR) MODEL IN PT.PLN (PERSERO) MEDAN CITY Rambe, Feby Mayori; Widyasari, Rina
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5533

Abstract

Electricity is a crucial component of modern life, where daily consumption fluctuates significantly. Uncertain electricity demand can lead to imbalances between supply and consumption, potentially causing energy wastage or power outages. To address this issue, a forecasting method capable of accurately predicting electricity load is essential. The Time Series Regression (TSR) model is applied for short-term electricity load forecasting by considering daily and weekly seasonal patterns. The forecasting results indicate that Monday and Tuesday have the highest electricity load, while Sunday has the lowest. When the Kolmogorov-Smirnov test is used to analyse the model, the p-value is 0.9608, which shows that the residuals have a normal distribution. The model's accuracy is assessed with a Root Mean Square Error (RMSE) value of 378.0069 MW, which is relatively high for a small dataset. Given the considerable forecasting error, further improvements such as hybrid models are recommended to enhance accuracy. The implementation of these forecasting results can help optimize electricity management and improve power distribution efficiency.
Analisis Harga Cabai Di Badan Pusat Statistik Provinsi Sumatera Utara Menggukan Metode Path Analys Hazrah, Ardaniah; Rambe, Feby Mayori; Siregar, Mei Sarah; Fransiska, Sintia; Widyasari, Rina
Jurnal IPTEK Bagi Masyarakat Vol 2 No 3 (2023)
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jibm.v2i3.537

Abstract

Selama melakukan kerja praktek di Badan Pusat Statistik Provinsi Sumatera, praktisi sering membantu memasukkan data pertanian seperti harga, luas panen, dan produksi cabai. Harga cabai di beberapa provinsi Sumut bervariasi, tergantung produksi dan luas panen cabai di daerah tersebut. Tujuan dari penelitian ini adalah untuk mengetahui seberapa besar pengaruh variabel luas panen cabai terhadap produksi dan harga rata-rata cabai di Provinsi Sumatera Utara. Data dalam penelitian ini merupakan data sekunder yang diperoleh dari BPS di 22 kabupaten di Sumatera Utara pada tahun 2021. Bentuk penelitian ini adalah analisis deskriptif dengan menggunakan metode pendekatan kuantitatif dengan membentuk persamaan regresi linier dalam analisis jalur dan pengolahan data statistik menggunakan SPSS 20. Terdapat dua pemodelan Dalam penelitian yang menggunakan analisis jalur, model pertama adalah luas panen sebagai variabel bebas terhadap produksi cabai sebagai variabel terikat, dan model kedua melihat pengaruh variabel terikat dan bebas terhadap variabel intervening yaitu rata-rata harga cabai. Hasil penelitian menunjukkan bahwa variabel luas panen berpengaruh nyata terhadap produksi cabai. Sedangkan produksi dan luas panen tidak berpengaruh nyata terhadap harga rata-rata cabai.
SHORT-TERM ELECTRICITY LOAD FORECASTING SEASONAL PATTERN USING TIME SERIES REGRESSION (TSR) MODEL IN PT.PLN (PERSERO) MEDAN CITY Rambe, Feby Mayori; Widyasari, Rina
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5533

Abstract

Electricity is a crucial component of modern life, where daily consumption fluctuates significantly. Uncertain electricity demand can lead to imbalances between supply and consumption, potentially causing energy wastage or power outages. To address this issue, a forecasting method capable of accurately predicting electricity load is essential. The Time Series Regression (TSR) model is applied for short-term electricity load forecasting by considering daily and weekly seasonal patterns. The forecasting results indicate that Monday and Tuesday have the highest electricity load, while Sunday has the lowest. When the Kolmogorov-Smirnov test is used to analyse the model, the p-value is 0.9608, which shows that the residuals have a normal distribution. The model's accuracy is assessed with a Root Mean Square Error (RMSE) value of 378.0069 MW, which is relatively high for a small dataset. Given the considerable forecasting error, further improvements such as hybrid models are recommended to enhance accuracy. The implementation of these forecasting results can help optimize electricity management and improve power distribution efficiency.
Co-Authors Fransiska, Sintia Hazrah, Ardaniah Siregar, Mei Sarah Widyasari, Rina