Evaluation of Machine Learning Models for Smart Grid Parameters: Performance Analysis of ARIMA and Bi-LSTM
Chen, Yuanhua; Bhutta, Muhammad Shoaib; Abubakar, Muhammad; Xiao, Dingtian; Almasoudi, Fahad M.; Naeem, Hamad; Faheem, Muhammad (2023-05-25)
Chen, Yuanhua
Bhutta, Muhammad Shoaib
Abubakar, Muhammad
Xiao, Dingtian
Almasoudi, Fahad M.
Naeem, Hamad
Faheem, Muhammad
MDPI
25.05.2023
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe20231009139397
https://urn.fi/URN:NBN:fi-fe20231009139397
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vertaisarvioitu
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Tiivistelmä
The integration of renewable energy resources into smart grids has become increasingly important to address the challenges of managing and forecasting energy production in the fourth energy revolution. To this end, artificial intelligence (AI) has emerged as a powerful tool for improving energy production control and management. This study investigates the application of machine learning techniques, specifically ARIMA (auto-regressive integrated moving average) and Bi-LSTM (bidirectional long short-term memory) models, for predicting solar power production for the next year. Using one year of real-time solar power production data, this study trains and tests these models on performance measures such as mean absolute error (MAE) and root mean squared error (RMSE). The results demonstrate that the Bi-LSTM (bidirectional long short-term memory) model outperforms the ARIMA (auto-regressive integrated moving average) model in terms of accuracy and is able to successfully identify intricate patterns and long-term relationships in the real-time-series data. The findings suggest that machine learning techniques can optimize the integration of renewable energy resources into smart grids, leading to more efficient and sustainable power systems.
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