Realistic Multi-Scale Modeling of Household Electricity Behaviors
To improve the management and reliability of power distribution networks, there is a strong demand for models simulating energy loads in a realistic way. In this paper, we present a novel multi-scale model to generate realistic residential load profiles at different spatial-temporal resolutions. By...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8573766/ |
| _version_ | 1819170299372896256 |
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| author | Lorenzo Bottaccioli Santa Di Cataldo Andrea Acquaviva Edoardo Patti |
| author_facet | Lorenzo Bottaccioli Santa Di Cataldo Andrea Acquaviva Edoardo Patti |
| author_sort | Lorenzo Bottaccioli |
| collection | DOAJ |
| description | To improve the management and reliability of power distribution networks, there is a strong demand for models simulating energy loads in a realistic way. In this paper, we present a novel multi-scale model to generate realistic residential load profiles at different spatial-temporal resolutions. By taking advantage of the information from census and national surveys, we generate statistically consistent populations of heterogeneous families with their respective appliances. Exploiting a bottom-up approach based on Monte Carlo Non-Homogeneous Semi-Markov, we provide household end-user behaviors and realistic households load profiles on a daily as well as on a weekly basis, for weekdays and weekends. The proposed approach overcomes the limitations of the state-of-the-art solutions that consider neither the time-dependency of the probability of performing specific activities in a house, nor their duration or are limited in the type of probability distributions they can model. On top of that, it provides outcomes that are not limited to a per-day basis. The range of available space and time resolutions span from single household to district and from second to year, respectively, featuring multi-level aggregation of the simulation outcomes. To demonstrate the accuracy of our model, we present experimental results obtained by simulating realistic populations in a period covering a whole calendar year and analyze our model’s outcome at different scales. Then, we compare such results with three different data-sets that provide real load consumption at the household, national, and European levels, respectively. |
| first_indexed | 2024-12-22T19:33:11Z |
| format | Article |
| id | doaj.art-4c360e30262a4dac97b7316929212a7b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:33:11Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4c360e30262a4dac97b7316929212a7b2022-12-21T18:15:03ZengIEEEIEEE Access2169-35362019-01-0172467248910.1109/ACCESS.2018.28862018573766Realistic Multi-Scale Modeling of Household Electricity BehaviorsLorenzo Bottaccioli0Santa Di Cataldo1https://orcid.org/0000-0002-6239-8945Andrea Acquaviva2Edoardo Patti3https://orcid.org/0000-0002-6043-6477Department of Control and Computer Engineering, Politecnico di Torino, Turin, ItalyDepartment of Control and Computer Engineering, Politecnico di Torino, Turin, ItalyEnergy Center Lab, Politecnico di Torino, Turin, ItalyDepartment of Control and Computer Engineering, Politecnico di Torino, Turin, ItalyTo improve the management and reliability of power distribution networks, there is a strong demand for models simulating energy loads in a realistic way. In this paper, we present a novel multi-scale model to generate realistic residential load profiles at different spatial-temporal resolutions. By taking advantage of the information from census and national surveys, we generate statistically consistent populations of heterogeneous families with their respective appliances. Exploiting a bottom-up approach based on Monte Carlo Non-Homogeneous Semi-Markov, we provide household end-user behaviors and realistic households load profiles on a daily as well as on a weekly basis, for weekdays and weekends. The proposed approach overcomes the limitations of the state-of-the-art solutions that consider neither the time-dependency of the probability of performing specific activities in a house, nor their duration or are limited in the type of probability distributions they can model. On top of that, it provides outcomes that are not limited to a per-day basis. The range of available space and time resolutions span from single household to district and from second to year, respectively, featuring multi-level aggregation of the simulation outcomes. To demonstrate the accuracy of our model, we present experimental results obtained by simulating realistic populations in a period covering a whole calendar year and analyze our model’s outcome at different scales. Then, we compare such results with three different data-sets that provide real load consumption at the household, national, and European levels, respectively.https://ieeexplore.ieee.org/document/8573766/Household load profileNon Homogeneous Semi-Markov ModelMonte Carlotime use surveyuse of energyload modeling |
| spellingShingle | Lorenzo Bottaccioli Santa Di Cataldo Andrea Acquaviva Edoardo Patti Realistic Multi-Scale Modeling of Household Electricity Behaviors IEEE Access Household load profile Non Homogeneous Semi-Markov Model Monte Carlo time use survey use of energy load modeling |
| title | Realistic Multi-Scale Modeling of Household Electricity Behaviors |
| title_full | Realistic Multi-Scale Modeling of Household Electricity Behaviors |
| title_fullStr | Realistic Multi-Scale Modeling of Household Electricity Behaviors |
| title_full_unstemmed | Realistic Multi-Scale Modeling of Household Electricity Behaviors |
| title_short | Realistic Multi-Scale Modeling of Household Electricity Behaviors |
| title_sort | realistic multi scale modeling of household electricity behaviors |
| topic | Household load profile Non Homogeneous Semi-Markov Model Monte Carlo time use survey use of energy load modeling |
| url | https://ieeexplore.ieee.org/document/8573766/ |
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