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 41
- Downloads 0
THE CONCEPT OF THE SMART CITY OF ASTANA: ENERGY-EFFICIENT TECHNOLOGIES AND SOLUTIONS FOR SUSTAINABLE DEVELOPMENT
Digitalization of the economy is one of the priority tasks set in the development strategies of Kazakhstan. Currently, there is a rapid change in information and communication technologies (ICT). Emerging changes affec...
MATHEMATICAL AND COMPUTER MODELS OF THE COVID-19 EPIDEMIC
The COVID-19 epidemic has gone down in history as an emergency of international importance. Currently, the number of people infected with coronavirus around the world continues to grow, and modeling such a complex system...
IMPACT OF THE COVID-19 PANDEMIC ON STUDENTS IN THE REPUBLIC OF KAZAKHSTAN
Kazakhstan, as many other countries in the world had to urgently switch to online learning when it became clear that the Covid-19 was a highly contagious disease and it was necessary to ‘social distance’ to slow down i...
MODELS, METHODS, AND MEANS OF REPRODUCTION OF EXPERT KNOWLEDGE IN INTELLIGENT SUPPORT SYSTEM BUILDING-TECHNICAL EXPERTISE
The paper is devoted to solving such a scientific and practical problem as the creation of computerized infocommunication systems for support building-technical expertise to determine the causes of destruction and defo...
DEVELOPMENT OF A METHOD FOR COMBINING DATA IN ORDER TO PREVENT DUPLICATION OF RECORDS IN THE DATABASE OF THE INFORMATION SYSTEM FOR THE DEVELOPMENT OF METHODOLOGICAL COMPETENCE OF TEACHERS OF IT DISCIPLINES
The article assesses the outcomes of digitization on higher education in Kazakhstan. Given current trends, there is a need to establish a dedicated focus on the ongoing professional growth of educators. A crucial measure...