scholarly journals Modeling Residential Energy Consumption

2021 ◽  
Vol 29 (2) ◽  
pp. 166-193
Author(s):  
Roya Gholami ◽  
Rohit Nishant ◽  
Ali Emrouznejad

Smart meters that allow information to flow between users and utility service providers are expected to foster intelligent energy consumption. Previous studies focusing on demand-side management have been predominantly restricted to factors that utilities can manage and manipulate, but have ignored factors specific to residential characteristics. They also often presume that households consume similar amounts of energy and electricity. To fill these gaps in literature, the authors investigate two research questions: (RQ1) Does a data mining approach outperform traditional statistical approaches for modelling residential energy consumption? (RQ2) What factors influence household energy consumption? They identify household clusters to explore the underlying factors central to understanding electricity consumption behavior. Different clusters carry specific contextual nuances needed for fully understanding consumption behavior. The findings indicate electricity can be distributed according to the needs of six distinct clusters and that utilities can use analytics to identify load profiles for greater energy efficiency.

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7523
Author(s):  
Minseok Jang ◽  
Hyun Cheol Jeong ◽  
Taegon Kim ◽  
Dong Hee Suh ◽  
Sung-Kwan Joo

Since January 2020, the COVID-19 pandemic has been impacting various aspects of people’s daily lives and the economy. The first case of COVID-19 in South Korea was identified on 20 January 2020. The Korean government implemented the first social distancing measures in the first week of March 2020. As a result, energy consumption in the industrial, commercial and educational sectors decreased. On the other hand, residential energy consumption increased as telecommuting work and remote online classes were encouraged. However, the impact of social distancing on residential energy consumption in Korea has not been systematically analyzed. This study attempts to analyze the impact of social distancing implemented as a result of COVID-19 on residential energy consumption with time-varying reproduction numbers of COVID-19. A two-way fixed effect model and demographic characteristics are used to account for the heterogeneity. The changes in household energy consumption by load shape group are also analyzed with the household energy consumption model. There some are key results of COVID-19 impact on household energy consumption. Based on the hourly smart meter data, an average increase of 0.3% in the hourly average energy consumption is caused by a unit increase in the time-varying reproduction number of COVID-19. For each income, mid-income groups show less impact on energy consumption compared to both low-income and high-income groups. In each family member, as the number of family members increases, the change in electricity consumption affected by social distancing tends to decrease. For area groups, large area consumers increase household energy consumption more than other area groups. Lastly, The COVID-19 impact on each load shape is influenced by their energy consumption patterns.


Author(s):  
Suchismita Bhattacharjee ◽  
Georg Reichard

Energy consumption in the United States’ residential sector has been marked by a steady growth over the past few decades, in spite of the implementation of several energy efficiency policies. To develop effective energy policies for the residential sector, it is of utmost importance to study the various factors affecting residential energy consumption. Earlier studies have identified and classified various individual factors responsible for the increment in household energy consumption, and have also analyzed the effect of socio-economic factors such as standard-of-living and income on overall household energy consumption. This research study identifies the socio-economic factors affecting household energy consumption. Potential reasons for the variation in residential energy efficiency consumption have been investigated in previous studies that only represent viewpoints of investigators analyzing specific problems. Additionally, a comprehensive review of literature failed to reveal existing research that had systematically explored the interdependencies among the various factors that could possibly affect residential energy consumption to give an overall perspective of these factors. Widely used academic and scholarly scientific databases were employed by two independent investigators to search for original research investigations. A total of more than 200 research studies were found by the investigators, with almost ninety percent agreement between the two investigators. Based on the inclusion and exclusion criteria of this research study the authors systematically reviewed 51 prominent research studies to create a comprehensive list of factors affecting residential energy consumption. The results are discussed in this review.


Author(s):  
Alice Sokolova ◽  
Baris Aksanli

Residential energy consumption constitutes a significant portion of the overall energy consumption. There are significant amount of studies that target to reduce this consumption, and these studies mainly create mathematical models to represent and regenerate the energy consumption of individual houses. Most of these models assume that the residential energy consumption can be classified and then predicted based on the household size. As a result, most of the previous studies suggest that household size can be treated as an independent variable which can be used to predict energy consumption. In this work, we test this hypothesis on a large residential energy consumption dataset that also includes demographic information. Our results show that other variables like income, geographic location, house type, and personal preferences strongly impact energy consumption and decrease the importance of household size because the household size can explain only 26.55% of the electricity consumption variation across the houses.


2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Alice Sokolova ◽  
Baris Aksanli

Residential energy consumption constitutes a significant portion of the overall energy consumption. There are significant amount of studies that target to reduce this consumption, and these studies mainly create mathematical models to represent and regenerate the energy consumption of individual houses. Most of these models assume that the residential energy consumption can be classified and then predicted based on the household size. As a result, most of the previous studies suggest that the household size can be treated as an independent variable which can be used to predict energy consumption. In this work, we test this hypothesis on a large residential energy consumption dataset that also includes demographic information. Our results show that other variables such as income, geographic location, house type, and personal preferences strongly impact energy consumption and decrease the importance of the household size because the household size can explain only 26.55% of the electricity consumption variation across the houses.


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