scholarly journals Impact of Time-Use Behaviour on Residential Energy Consumption in the United Kingdom

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6286
Author(s):  
Máté János Lőrincz ◽  
José Luis Ramírez-Mendiola ◽  
Jacopo Torriti
Keyword(s):  
Energy Consumption ◽  
Time Use ◽  
Energy Demand ◽  
Full Time ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Total Energy Consumption ◽  
Current Time ◽  
Starting Point ◽  
Part Time

In order to have the best possible chance of achieving ‘decent work’ and ‘climate action’ as laid forth in the UN Sustainable Development Goals, government and policy makers must pay close attention to current time-use patterns, as well as the way these might change in the near future. Here we contribute to the existing literature on time-use behaviour through a systematic exploration of the relationship between working patterns and energy consumption from the perspective of time-use. Our starting point is the premise that different work arrangements impact the timing of energy demand not only in workplaces, but also at home. Using the data from the 2014–2015 UK time-use survey, we were able to capture patterns of time-use behaviours and to assess their relationship with daily energy consumption. We propose a systematic time-use-based approach for estimating residential energy consumption with regards to activity timing, activity location, activity coordination, and appliance type. We use this method to discover patterns in residential activities and energy consumption, as well as the causal relationship between residential energy consumption and work patterns. In this study, we unpack the heterogeneity in the work–energy relationship, particularly when comparing full-time and part-time workers. Our results suggest that full-time employees have a higher potential to reduce their energy use compared to part-time employees. We also discover a non-linear change in total energy consumption for respondents with varying levels of work time. Energy consumption reductions associated with differences in work schedules are greatest during the first few hours of the workday, but then level off. Our findings suggests that time-use data can provide useful insights for evaluating and possibly designing energy and labour-market policies.

Causal Effect of Time-Use Behavior on Residential Energy Consumption in China

2020 ◽  
Vol 175 ◽  
pp. 106706
Author(s):  
Biying Yu ◽  
Xiaojuan Yang ◽  
Qingyu Zhao ◽  
Jinxiao Tan
Keyword(s):  
Energy Consumption ◽  
Time Use ◽  
Causal Effect ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Use Behavior

scholarly journals Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4722
Author(s):  
Seok-Jun Bu ◽  
Sung-Bae Cho
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Energy Consumption ◽  
Energy Demand ◽  
Multivariate Time Series ◽  
Learning Model ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Sensor Signals ◽  
Deep Learning Model

Predicting residential energy consumption is tantamount to forecasting a multivariate time series. A specific window for several sensor signals can induce various features extracted to forecast the energy consumption by using a prediction model. However, it is still a challenging task because of irregular patterns inside including hidden correlations between power attributes. In order to extract the complicated irregular energy patterns and selectively learn the spatiotemporal features to reduce the translational variance between energy attributes, we propose a deep learning model based on the multi-headed attention with the convolutional recurrent neural network. It exploits the attention scores calculated with softmax and dot product operation in the network to model the transient and impulsive nature of energy demand. Experiments with the dataset of University of California, Irvine (UCI) household electric power consumption consisting of a total 2,075,259 time-series show that the proposed model reduces the prediction error by 31.01% compared to the state-of-the-art deep learning model. Especially, the multi-headed attention improves the prediction performance even more by up to 27.91% than the single-attention.

scholarly journals Evaluating the Age-Energy Consumption Profile in Residential Buildings

2018 ◽  
Author(s):  
Hossein Estiri ◽  
Emilio Zagheni
Keyword(s):  
Energy Consumption ◽  
Life Course ◽  
Energy Demand ◽  
Population Aging ◽  
Residential Buildings ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Residential Energy Consumption Survey ◽  
The Life Course ◽  
Consumption Profile

Age is an important proxy for many life course trajectories. The relationship between energy consumption and age is complex and understudied. We evaluated the existence and determinants of an age-energy consumption profile in the U.S. residential sector, using microdata from four waves of the Residential Energy Consumption Survey (RECS) in 1987, 1990, 2005, and 2009. We constructed pseudo cohorts from Bayesian generalized linear model estimates to draw micro-profiles for energy consumption across the life course. Overall, we found that residential energy consumption increases over the life course. Much of the increase in energy consumption is due to housing size. Variations in the age-energy consumption micro-profiles can be described by concave and convex functions. In contrast to previous research that suggested that population aging would reduce energy demand, our results indicate that changing population age structure could amplify residential energy demand.

scholarly journals Spatiotemporal Patterns and Influencing Mechanism of Urban Residential Energy Consumption in China

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3864
Author(s):  
Qiucheng Li ◽  
Jiang Hu ◽  
Bolin Yu
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Decomposition Analysis ◽  
Theil Index ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Index Method ◽  
Inequality Measures ◽  
Influencing Mechanism ◽  
Per Capita

The residential sector has become the second largest energy consumer in China. Urban residential energy consumption (URE) in China is growing rapidly in the process of urbanization. This paper aims to reveal the spatiotemporal dynamic evolution and influencing mechanism of URE in China. The spatiotemporal heterogeneity of URE during 2007–2018 is explored through Kernel density estimation and inequality measures (i.e., Gini coefficient, Theil index, and mean logarithmic deviation). Then, with several advantages over traditional index decomposition analysis approaches, the Generalized Divisia Index Method (GDIM) decomposition is employed to investigate the impacts of eight driving factors on URE. Furthermore, the national and provincial decoupling relationships between URE and residential income increase are studied. It is found that different provinces’ URE present a significant agglomeration effect; the interprovincial inequality in URE increases and then decreases during the study period. The GDIM decomposition results indicate the income effect is the main positive factor driving URE. Besides, urban population, residential area, per capita energy use, and per unit area energy consumption positively influence URE. By contrast, per capita income, energy intensity, and residential density have negative effects on URE. There is evidence that only three decoupling states, i.e., weak decoupling, strong decoupling, and expansive negative decoupling, appear in China during 2007–2018. Specifically, weak decoupling is the dominant state among different regions. Finally, some suggestions are given to speed up the construction of energy-saving cities and promote the decoupling process of residential energy consumption in China. This paper fills some research gaps in urban residential energy research and is important for China’s policymakers.

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