Enhancing Electricity Consumption Forecasting in The Republic of Kazakhstan Using Machine Learning

Accurate electricity consumption forecasting is critical for optimizing energy management and ensuring grid stability. This study uses advanced machine learning techniques to enhance electricity consumption forecasting in the Republic of Kazakhstan. The research analyzes historical electricity consumption data from 2002 to 2022. Considering seasonal and temporal dependencies. Various forecasting models, including Holt-Winters, Seasonal ARIMA (SARIMA), and Long Short-Term Memory (LSTM) networks, are applied and compared in terms of accuracy and reliability. The results indicate that while traditional statistical models effectively capture seasonal patterns, machine learning-based approaches, particularly LSTM, demonstrate superior performance in identifying complex nonlinear trends. The study discusses the practical implications of accurate electricity consumption forecasting for energy management, demand-side optimization, and policymaking. The findings contribute to developing intelligent analytical frameworks for improving energy efficiency and sustainability in Kazakhstan’s power sector. This study enhances electricity consumption forecasting in Kazakhstan using machine learning models, improving accuracy and energy management. Scientifically, it advances predictive analytics in power systems. Practically, it aids grid stability and demand planning. And sustainability. Internationally, the findings contribute to global forecasting methodologies, benefiting energy sectors worldwide. LSTM outperforms traditional models, offering robust solutions for dynamic electricity demand. This study uses advanced machine learning techniques to improve electricity consumption forecasting in the Republic of Kazakhstan. Historical monthly data from 2002 to 2022 were collected from the National Statistics Bureau. We compared statistical models (Holt-Winters, SARIMA) with a Long Short-Term Memory (LSTM) neural network. Results show that while classical methods effectively capture seasonal trends, LSTM more accurately models nonlinearities and longer-term dependencies. The implications include enhanced planning for energy providers and policymakers, leading to better demand-side management and grid stability. Our findings contribute to developing intelligent forecasting systems in Kazakhstan’s power sector and provide an example for other regions with similar energy challenges.