Deep learning approaches for energy consumption forecasting: analyzing stress factors and optimizing models for future demand

Energy demand forecasting is crucial to the creation of reliable and sustainable energy systems, given the rising global consumption and the increasing integration of renewable energy sources. In this study, we evaluate and compare a number of machine learning (ML) and deep learning (DL) techniques for energy consumption prediction. Our findings demonstrate the exceptional performance of DL models, particularly autoencoders, with an R² value of 0.9686. By using Optuna to adjust hyperparameters, we significantly enhance model performance, as evidenced by CatBoost’s rise from R² = 0.7415 to 0.8575. The study examines hybrid approaches, which blend traditional statistical methods like ARIMA with machine learning techniques like SVMs and ANNs, in addition to spatiotemporal modeling techniques. We consider significant external factors, such as population trends, economic indicators, and weather patterns. The primary challenges identified are computing loads, data quality issues, and scalability constraints. We recommend that future research give careful uncertainty quantification, a better comprehension of the dynamics of renewable energy integration, and interpretable models top priority in light of our findings. Our results indicate that, despite DL models’ superior predictive performance, optimized machine learning techniques provide a computationally cost-effective alternative. Because of this, they are particularly helpful in real-world energy planning applications where resources might be scarce.