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Start Over Descriptor "BUILDING AND CONSTRUCTION" Publisher elsevier bv
152,245 results on '"BUILDING AND CONSTRUCTION"'

48. Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method

Author

Mengjie Han, Fatemeh Johari, Pei Huang, and Xingxing Zhang

Subjects
Renewable Energy, Sustainability and the Environment, large-scale buildings, Transportation, Data generation, Building and Construction, time series decomposition, hourly electricity demand, Energy Systems, Energisystem, Civil and Structural Engineering
Abstract

Household electricity demand has substantial impacts on local grid operation, energy storageand the energy performance of buildings. Hourly demand data at district or urban level helpsstakeholders understand the demand patterns from a granular time scale and provides robustevidence in energy management. However, such type of data is often expensive and time-consuming to collect, process and integrate. Decisions built upon smart meter data have todeal with challenges of privacy and security in the whole process. Incomplete data due toconfidentiality concerns or system failure can further increase the difficulty of modeling and optimization. In addition, methods using historical data to make predictions can largely varydepending on data quality, local building environment, and dynamic factors. Consideringthese challenges, this paper proposes a statistical method to generate hourly electricitydemand data for large-scale single-family buildings by decomposing time series data andrecombining them into synthetics. The proposed method used public data to captureseasonality and the distribution of residuals that fulfill statistical characteristics. A referencebuilding was used to provide empirical parameter settings and validations for the studiedbuildings. An illustrative case in a city of Sweden using only annual total demand waspresented for deploying the proposed method. The results showed that the proposed methodcan mimic reality well and represent a high level of similarity to the real data. The averagemonthly error for the best month reached 15.9% and the best one was below 10% among 11tested months. Less than 0.6% improper synthetic values were found in the studied region.

Published
2023
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