Residential Energy Consumption Prediction Using Inter-Household Energy Data and Socioeconomic Information

2021 ◽  
Author(s):  
Pascal A. Schirmer ◽  
Christian Geiger ◽  
Iosif Mporas
Keyword(s):  
Energy Consumption ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Household Energy ◽  
Energy Data ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

scholarly journals Empirical Analysis of the Impact of COVID-19 Social Distancing on Residential Electricity Consumption Based on Demographic Characteristics and Load Shape

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7523
Author(s):  
Minseok Jang ◽  
Hyun Cheol Jeong ◽  
Taegon Kim ◽  
Dong Hee Suh ◽  
Sung-Kwan Joo
Keyword(s):  
Energy Consumption ◽  
Electricity Consumption ◽  
Time Varying ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Household Energy ◽  
Social Distancing ◽  
Household Energy Consumption ◽  
Load Shape ◽  
The Impact

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.

scholarly journals Modeling Residential Energy Consumption

2021 ◽  
Vol 29 (2) ◽  
pp. 166-193
Author(s):  
Roya Gholami ◽  
Rohit Nishant ◽  
Ali Emrouznejad
Keyword(s):  
Energy Consumption ◽  
Service Providers ◽  
Electricity Consumption ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Smart Meters ◽  
Household Energy ◽  
Consumption Behavior ◽  
Data Mining Approach ◽  
Residential Characteristics

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.

Socio-Economic Factors Affecting Individual Household Energy Consumption: A Systematic Review

2011 ◽  
Author(s):  
Suchismita Bhattacharjee ◽  
Georg Reichard
Keyword(s):  
Energy Consumption ◽  
Research Study ◽  
Economic Factors ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Household Energy ◽  
Factors Affecting ◽  
Household Energy Consumption ◽  
Residential Sector ◽  
Socio Economic Factors

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.

scholarly journals Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea

2019 ◽  
Vol 12 (1) ◽  
pp. 109 ◽  
Author(s):  
Mansu Kim ◽  
Sungwon Jung ◽  
Joo-won Kang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
Energy Consumption ◽  
Prediction Models ◽  
Residential Buildings ◽  
Residential Energy ◽  
Artificial Neural ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

When researching the energy consumption of residential buildings, it is becoming increasingly important to consider how residents use energy. With the advancement of computing power and data analysis techniques, it is now possible to analyze user information using big data techniques. Here, we endeavored to integrate user information with the physical characteristics of residential buildings to analyze how these elements impact energy consumption. Regression analysis was conducted to accurately identify the impact of each element on energy consumption. It was found that six elements were influential in all seasons: the number of exterior walls, housing direction, housing area, number of years occupied, number of household members, and the occupation of the household head. The elements that had an impact in each period were then derived. Based on the results of the regression analysis, input variables for the training of an artificial neural network (ANN) model were selected for each period, and residential energy consumption prediction models were implemented based on actual consumption. The elements identified as those affecting energy consumption, through regression analysis, can be used for implementing prediction models with advanced forms. This study is significant in that we derived influential elements from an integrative perspective.

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