# Enhancing Microgrid Performance Prediction with Attention-based Deep Learning Models

arXiv: https://arxiv.org/abs/2407.14984 · HTML: https://mlsanity.com/paper/2407.14984/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)](https://mlsanity.com/paper/2004.04523/) (2020)
- [A comparison of LSTM and GRU networks for learning symbolic sequences](https://mlsanity.com/paper/2107.02248/) (2021)
- [A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer](https://mlsanity.com/paper/2305.17510/) (2023)
- [TLU-Net: A Deep Learning Approach for Automatic Steel Surface Defect Detection](https://mlsanity.com/paper/2101.06915/) (2021)
- [LSTM-CNN Network for Audio Signature Analysis in Noisy Environments](https://mlsanity.com/paper/2312.07059/) (2023)
- [An Automated Machine Learning Approach to Inkjet Printed Component Analysis: A Step Toward Smart Additive Manufacturing](https://mlsanity.com/paper/2404.04623/) (2024)

## Abstract (arXiv)

In this research, an effort is made to address microgrid systems' operational challenges, characterized by power oscillations that eventually contribute to grid instability. An integrated strategy is proposed, leveraging the strengths of convolutional and Gated Recurrent Unit (GRU) layers. This approach is aimed at effectively extracting temporal data from energy datasets to improve the precision of microgrid behavior forecasts. Additionally, an attention layer is employed to underscore significant features within the time-series data, optimizing the forecasting process. The framework is anchored by a Multi-Layer Perceptron (MLP) model, which is tasked with comprehensive load forecasting and the identification of abnormal grid behaviors. Our methodology underwent rigorous evaluation using the Micro-grid Tariff Assessment Tool dataset, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (r2-score) serving as the primary metrics. The approach demonstrated exemplary performance, evidenced by a MAE of 0.39, RMSE of 0.28, and an r2-score of 98.89% in load forecasting, along with near-perfect zero state prediction accuracy (approximately 99.9%). Significantly outperforming conventional machine learning models such as support vector regression and random forest regression, our model's streamlined architecture is particularly suitable for real-time applications, thereby facilitating more effective and reliable microgrid management.