Analysis of consumer behavior using energy consumption data

This thesis provides an understanding of regional particle behavior by focusing on time-dependent energy consumption and various demographic mass analysis of specific regions. Today, energy consumption and consumer behavior are critical for sustainable energy management and social welfare. In this context, this thesis focuses on the districts of Istanbul and includes data on natural gas, water, households, number of gas subscribers, literacy rates, rental housing prices, households receiving social assistance, annual medical waste amounts and domestic waste amounts over time. It aims to analyze its effects on regional energy consumption. The thesis aims to examine the relationship between regional energy consumption and consumer behavior through critical data such as natural gas, water, waste management, literacy rates and income status. In the analysis, statistical and machine learning methods are used to understand energy consumption patterns over a certain period of time and the relationship of these patterns with demographic factors. Additionally, regression analyzes and data mining techniques are applied to determine the factors affecting regional energy consumption and make inferences. The findings will highlight the factors affecting regional energy consumption and reveal the factors that shape the consumer behavior of the local people. The thesis aims to contribute to our understanding of these connections, which are important for energy planning and sustainability, and to develop strategies to optimize regional energy consumption. The thesis also uses various statistical and data mining methods to understand the effects of the Covid-19 period on energy consumption and consumer behavior. In the thesis, applications such as interpreting time series on graphs using data, making predictions for the future through graphs, classification with machine learning techniques and repeating this process with Manifold Learning were carried out. Analyzes of the data revealed that average energy consumption per person is related to income rather than literacy. It was observed that there was an increase in water consumption and medical waste during the pandemic period. This study will provide valuable information for policy makers, academics and industry experts in the field of energy sector and consumer behavior and will support efforts in sustainable energy management. The thesis aims to contribute to the development of energy policies and sustainability strategies by determining the factors affecting energy consumption in the districts of Istanbul. The results will guide local governments, This thesis provides an understanding of regional particle behavior by focusing on time-dependent energy consumption and various demographic mass analysis of specific regions. Today, energy consumption and consumer behavior are critical for sustainable energy management and social welfare. In this context, this thesis focuses on the districts of Istanbul and includes data on natural gas, water, households, number of gas subscribers, literacy rates, rental housing prices, households receiving social assistance, annual medical waste amounts and domestic waste amounts over time. It aims to analyze its effects on regional energy consumption. The thesis aims to examine the relationship between regional energy consumption and consumer behavior through critical data such as natural gas, water, waste management, literacy rates and income status. In the analysis, statistical and machine learning methods are used to understand energy consumption patterns over a certain period of time and the relationship of these patterns with demographic factors. Additionally, regression analyzes and data mining techniques are applied to determine the factors affecting regional energy consumption and make inferences. The findings will highlight the factors affecting regional energy consumption and reveal the factors that shape the consumer behavior of the local people. The thesis aims to contribute to our understanding of these connections, which are important for energy planning and sustainability, and to develop strategies to optimize regional energy consumption. The thesis also uses various statistical and data mining methods to understand the effects of the Covid-19 period on energy consumption and consumer behavior. In the thesis, applications such as interpreting time series on graphs using data, making predictions for the future through graphs, classification with machine learning techniques and repeating this process with Manifold Learning were carried out. Analyzes of the data revealed that average energy consumption per person is related to income rather than literacy. It was observed that there was an increase in water consumption and medical waste during the pandemic period. This study will provide valuable information for policy makers, academics and industry experts in the field of energy sector and consumer behavior and will support efforts in sustainable energy management. The thesis aims to contribute to the development of energy policies and sustainability strategies by determining the factors affecting energy consumption in the districts of Istanbul. The results will guide local governments, energy companies and planners in creating effective policies on regional energy consumption and will guide future research. The study may shed light on the development of strategies to increase energy efficiency at the regional level. This analysis conducted on the districts of Istanbul may inspire the implementation of similar studies in similar large cities and regions.