Demand Forecasting Mathematical Models for Residential Electricity Consumption Considering Ambient Temperature

This study presents an in-depth analysis of mathematical models for forecasting residential electricity consumption, with a particular focus on the influence of ambient temperature. The research is contextualized within Ukraine's unique socio-economic and climatic conditions, highlighting the significance of tailored forecasting models. The methodology encompasses a comprehensive review of various forecasting approaches, ranging from traditional statistical analysis to advanced techniques involving artificial intelligence and machine learning. A key aspect of this study is the integration of environmental factors, notably temperature variations, in understanding and predicting household energy demands. We have explored the intricate relationship between electricity consumption patterns and ambient temperature fluctuations. The data, primarily drawn from Ukraine's national energy provider UkrEnergo, and includes detailed monthly consumption statistics. The analysis is further enriched by incorporating average monthly temperature data from climatecharts.net, offering a nuanced perspective on the interplay between weather conditions and energy usage. The results underscore the need for forecasting models that can adapt to rapid changes in consumption patterns, as evidenced during extraordinary events like the COVID-19 pandemic. The research provides valuable insights into the development of robust and accurate forecasting models for residential electricity consumption. These models are essential for efficient energy management and planning, particularly in the face of environmental and societal changes. The study not only contributes to the academic field but also has practical implications for energy policymakers and stakeholders in Ukraine and similar regions.