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All Journal Bulletin of Electrical Engineering and Informatics Andalasian International Journal of Applied Science, Engineering, and Technology
Ilhamdi Rusydi, Muhammad
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Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Mechanical Engineering

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The Prediction of Electrical Grid Stability Using Naïve Bayes and K-Means Algorithm Baik Budi; Ilhamdi Rusydi, Muhammad; Arya Witama, Reivan; Hesti Ramadhamy, Queen; Budiman, Refki
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 5 No. 2 (2025): July 2025
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v5i02.223

Abstract

This study explores the use of Naive Bayes and k-means algorithms to predict and analyzed stability of the electrical grid. Data set for this research is public dataset from Kaggle. The main goal of the research is to develop an accurate and efficient predictive model. Naive Bayes was chosen it has ability to handle independent features and also have a compatibility with highdimensional data. The implementation was carried out using Python in Google Colab, with data preprocessing that included feature normalization and an 80:20 train-test split. The Gaussian Naive Bayes model was used for system stability classification. The results demonstrate excellent model performance, with an accuracy of 97.35%, precision of 98.91%, recall of 97.02%, and an F1-score of 97.95%. The confusion matrix reveals the model's ability to classify "stable" and "unstable" conditions with minimal prediction errors.
An internet of things-based weather system for short-term solar and wind power forecasting using double moving average Syafii, Syafii; Nur Izrillah, Imra; Aulia, Aulia; Ilhamdi Rusydi, Muhammad
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9852

Abstract

This article presents the design and implementation of an internet of things (IoT)-based weather forecasting system aimed at optimizing operational planning for renewable energy generation. The system leverages a Raspberry Pi as its central controller, integrating pyranometer and anemometer sensors for real-time data collection and predictive analytics. Utilizing the double moving average method, the system provides accurate short-term forecasts of solar and wind power outputs, which are crucial for addressing the intermittency challenges of renewable energy sources. The integration with the Blynk platform ensures user-friendly data visualization and accessibility. Results from a three-day testing phase reveal the system's high accuracy, with prediction errors of 8.79% for solar power and 16.49% for wind power. These findings underscore the system's potential to enhance energy planning, improve efficiency, and support sustainability goals. By enabling data-driven decision-making, this IoT-based forecasting system offers a scalable solution for advancing renewable energy integration into the power grid.
Co-Authors Arya Witama, Reivan Aulia Aulia Baik Budi Budiman, Refki Hesti Ramadhamy, Queen Nur Izrillah, Imra Syafii .