DEEP RECURRENT NEURAL NETWORKS IN ENERGY DEMAND FORECASTING: A CASE STUDY OF KAZAKHSTAN'S ELECTRICAL CONSUMPTION
Journal Title: Scientific Journal of Astana IT University - Year 2023, Vol 16, Issue 16
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.
Authors and Affiliations
Samat Kabdygali, Ruslan Omirgaliyev, Timur Tursynbayev, Korhan Kayisli, Nurkhat Zhakiyev
Keywords
Related Articles
- EP ID EP729506
- DOI https://doi.org/10.37943/16YIKA8050
- Views 53
- Downloads 0
STRATEGIES AND OPERATION PRINCIPLES OF LOGISTIC INFORMATION SYSTEMS
Over the last few years the so-called new logistic technologies are rapidly developed. Information systems hold the central position in these technologies. Development of logistics in the developed countries not least...
EXPERIMENTAL VERIFICATION OF THE EFFECTIVENESS OF TEACHING METHODS USING ADAPTIVE MATHEMATICS TEACHING
The article presents theoretical and empirical results of the study of the advantages of adaptive learning. The practice of creating and organizing adaptive learning for students using the «Moodle» platform is consider...
MATHEMATICAL FRAMEWORK FORMULATION AND IMPLEMENTATION FOR HYPERSPECTRAL AEROSPACE IMAGES PROCESSING
This paper proposes a preprocessing algorithm for aerospace hyperspectral images based on a mathematical apparatus effectively applied in pre-compression transformation problems. In particular, several methods have been...
MODELLING OF EROSION OF THE AGILE LEADERSHIP PROJECT MANAGER COMPETENCES
The structure and functions of mechanisms of development and erosion of competencies in innovative projects of implementation of information and communication technologies are considered. The factors of development and...
STRUCTURE OF THE PROJECT-ORIENTED ORGANIZATION ENERGY ENTROPY
This study presents the universal formalization of energy entropy for various organizations and its expression for project-oriented organizations. The energy entropy of organizations is determined by information entropy,...