Deep Recurrent Neural Networks in Energy Demand Forecasting: A Case Study of Kazakhstan's Electrical Consumption

Abstract

The critical transformation of the energy sector demands innovative approaches to ensure the reliability and efficiency of energy systems. In this pursuit, this study delved into the potential of Deep Recurrent Neural Networks (DRNNs) for forecasting energy demand, using a comprehensive dataset detailing Kazakhstan's electrical consumption over a span of two years. Traditional statistical models have historically played a role in energy demand prediction, but the growing intricacy of the energy landscape calls for more advanced solutions. The paper presented a comparison of the DRNN with other traditional and machine learning models and highlighted the superior performance of DRNNs, especially in capturing complex temporal relationships. The energy sector is confronting unprecedented challenges due to population growth and the integration of diverse energy sources, leading to increased demand and system strains. Accurate energy demand prediction is essential for system reliability. Traditional models, though widely used, often overlook intricate variables like weather patterns and temporal factors. Through rigorous methodology, encompassing exploratory data analysis, feature engineering, and hyperparameter optimization, an optimized DRNN model was developed. The results demonstrated the DRNN's exceptional capability in processing complex time-series data, as evidenced by its attainment of an R-squared value of 83.6%. Additionally, it achieved Mean Absolute Errors and Root Mean Squared Errors of less than 2%. However, there were noticeable deviations in some predictions, suggesting areas for refinement. This research underscores the significance of DRNNs in energy demand prediction, highlighting their advantages over traditional models while also noting the need for ongoing optimization. The findings underscore DRNN's promise as a robust forecasting tool, pivotal for the energy sector's future resilience and efficiency.