Assessment of the impact of demographic, economic, and climatic factors on long-term forecasting of electricity consumption - Kakhraman R. Allaev; Tokhir F. Makhmudov; Denis Y. Losev (PROBLEMS OF ENERGY AND SOURCES SAVING, 2026)
Relevance: long-term electricity demand forecasting is a crucial task for efficient energy resource planning, de velopment of energy supply strategies, and ensuring the stability of the energy system. In the context of global changes such as changes in ambient temperature, population growth, and economic development dynamics, the accuracy of electricity demand forecasts becomes particularly important for making informed decisions in energy strategy formulation. Demographic, economic, and climatic factors shape the overall electricity consumption picture, but the impact of each of these factors can vary significantly depending on specific conditions. The weight coefficients of factors, calculated within regression models, allow for a quantitative assessment of the impact of each factor on long-term changes in electricity consumption. Thus, the assessment of the weight coeffi cients of factors is an important part of the analysis, as it allows improving the accuracy of forecasts and identify ing the most significant elements that require attention when planning electricity demand in the long-term fore casting. Therefore, the study of weight coefficients of demographic, economic, and climatic factors and their impact on electricity demand forecasting is an important step toward more accurate and grounded forecasts in the energy sector, which has practical significance for the development of sustainable and adaptable energy supply models in changing conditions. Aim: to analyze the main factors influencing the long-term forecasting of electricity consumption. Methods: there were used statistical methods of analysis and forecasting. Results: the analysis revealed that current electricity consumption has the greatest impact on long-term consump tion (59.54%), followed by demographic (21.91%) and economic factors (12.28%). The climatic factor has the least influence (6.27%). The model demonstrated high accuracy (RMSE = 0.1611) and can be applied for long term forecasting in the power system.