Machine Learning and Deep Learning Approaches for Energy Prediction: A Systematic Literature Review
DOI:
10.33395/sinkron.v8i4.14208Keywords:
Attention Mechanism, Deep Learning, Energy Forecasting, Hybrid Models, Machine Learning, Renewable Energy, Smart GridAbstract
This paper offers a literature review on the application of Machine Learning (ML) and Deep Learning (DL) techniques in energy prediction. Contemporary energy systems' challenges, such as load fluctuations and uncertainties linked to renewable energy sources, render traditional methods like ARIMA and linear regression insufficient. The objective of this paper is to identify the most widely used ML and DL approaches, compare their performance against conventional methods, and explore the implementation challenges along with potential solutions. The methodology for this literature review involves analyzing publications from Scopus, IEEE Xplore, and ScienceDirect covering the period from 2019 to 2024. The findings indicate that DL methods, particularly Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, are effective in handling sequential data, while hybrid models like CNN-GRU enhance prediction accuracy in innovative grid applications. Challenges identified include overfitting and data complexity, which can be addressed through regularization techniques and computational optimization using GPUs. In conclusion, this paper asserts that ML and DL play a significant role in improving prediction accuracy and facilitating the transition towards sustainable energy and smart grids. To further enhance performance in the future, the paper recommends the development of ensemble models and the integration of attention mechanisms.
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