Smart City Gnosys
Smart city article details
| Title | Modeling Residential Electricity Consumption From Public Demographic Data For Sustainable Cities |
|---|---|
| ID_Doc | 37602 |
| Authors | Ali M.; Prakash K.; Macana C.; Bashir A.K.; Jolfaei A.; Bokhari A.; Klemeš J.J.; Pota H. |
| Year | 2022 |
| Published | Energies, 15, 6 |
| DOI | http://dx.doi.org/10.3390/en15062163 |
| Abstract | Demographic factors, statistical information, and technological innovation are prominent factors shaping energy transitions in the residential sector. Explaining these energy transitions re-quires combining insights from the disciplines investigating these factors. The existing literature is not consistent in identifying these factors, nor in proposing how they can be combined. In this paper, three contributions are made by combining the key demographic factors of households to estimate household energy consumption. Firstly, a mathematical formula is developed by considering the demographic determinants that influence energy consumption, such as the number of persons per household, median age, occupancy rate, households with children, and number of bedrooms per household. Secondly, a geographical position algorithm is proposed to identify the geographical locations of households. Thirdly, the derived formula is validated by collecting demographic factors of five statistical regions from local government databases, and then compared with the electricity consumption benchmarks provided by the energy regulators. The practical feasibility of the method is demonstrated by comparing the estimated energy consumption values with the electricity consumption benchmarks provided by energy regulators. The comparison results indicate that the error between the benchmark and estimated values for the five different regions is less than 8% (7.37%), proving the efficacy of this method in energy consumption estimation processes. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
| Author Keywords | demographic data; energy transitions; information management; residential sector; smart cities |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
55778  | 0.893 | Chalal M.L.; Benachir M.; White M.; Shahtahmassebi G.; Cumberbatch M.; Shrahily R. | The Impact Of The Uk Household Life-Cycle Transitions On The Electricity And Gas Usage Patterns | Renewable and Sustainable Energy Reviews, 80 (2017) |
| 23402 | 0.868 | Pincetl S.; Gustafson H.; Federico F.; Fournier E.D.; Cudd R.; Porse E. | Energy Use In Cities: A Roadmap For Urban Transitions | Energy Use in Cities: A Roadmap for Urban Transitions (2020) |
| 33403 | 0.862 | Afzalan M.; Jazizadeh F. | Investigating The Appliance Use Patterns On The Households' Electricity Load Shapes From Smart Meters | Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (2019) |
| 52403 | 0.857 | Roth J.; Bailey A.; Choudhary S.; Jain R.K. | Spatial And Temporal Modeling Of Urban Building Energy Consumption Using Machine Learning And Open Data | Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (2019) |
| 42791 | 0.852 | Zou Y.; Xie W.; Lou S.; Huang Y.; Xia D.; Yang X.; Feng C. | Prediction Model Establishment For Residential Community Occupancy Considering Urban Environment | Journal of Building Engineering, 96 (2024) |
| 14511 | 0.851 | Savvopoulos A.; Kalogeras G.; Anagnostopoulos C.; Alexakos C.; Sioutas S.; Kalogeras A.P. | Cluster-Based Energy Load Profiling On Residential Smart Buildings | IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2020-September (2020) |