Al-Based Techniques to Predict Electricity Consumption in Algeria Using RNN and LSTM Approaches
| المصدر: | مجلة الاقتصاد التطبيقي والإحصاء |
|---|---|
| الناشر: | المدرسة الوطنية العليا للإحصاء والاقتصاد التطبيقي |
| المؤلف الرئيسي: | Brahimi, Nadjib (Author) |
| مؤلفين آخرين: | Zakane, Ahmed (Co-Author) |
| المجلد/العدد: | مج21, ع2 |
| محكمة: | نعم |
| الدولة: |
الجزائر |
| التاريخ الميلادي: |
2024
|
| الشهر: | ديسمبر |
| الصفحات: | 41 - 51 |
| ISSN: |
1112-234x |
| رقم MD: | 1542766 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EcoLink |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Artificial Intelligence | Energy Forecasting | Machine Learning | Deep Learning | RNN-LSTM
|
| رابط المحتوى: |
الناشر لهذه المادة لم يسمح بإتاحتها. |
| المستخلص: |
Artificial Intelligence (AI) has emerged as a game-changer in various industries, and the energy sector is no exception. By leveraging machine learning (ML) and Deep Learning forecasting models, the energy industry is experiencing significant advancements in efficiency, sustainability, and reliability. This article delves into the application of a machine-learning model employing a deep-learning forecasting technique in the realm of energy and its transformative impact on the sector. The forecasting of daily Electricity power consumption improves the quality, reliability, and stability of the power system. This study aims to develop an LSTM technique for daily forecasting of electricity power consumption. Furthermore, RNN is used to daily perform predictions of electricity power consumption, we will use daily data on Algeria's electricity demand that was collected between January 2000 and December 2022. We run a comparative analysis for all these techniques. We found that the Deep learning technique (RNN) has better forecasting accuracy than other developed techniques in terms of better-performing goodness-of-fit metrics. |
|---|---|
| ISSN: |
1112-234x |
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