Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniques
Technological advances, innovation and the new industry 4.0 paradigm guide Distribution System Operators towards a competitive market that requires the articulation of flexible demand response systems. The lack of measurement and standardization systems in the industry process chain in developing co...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546823000125 |
| _version_ | 1797802129194221568 |
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| author | Marco Toledo-Orozco C. Celi F. Guartan Arturo Peralta Carlos Álvarez-Bel D. Morales |
| author_facet | Marco Toledo-Orozco C. Celi F. Guartan Arturo Peralta Carlos Álvarez-Bel D. Morales |
| author_sort | Marco Toledo-Orozco |
| collection | DOAJ |
| description | Technological advances, innovation and the new industry 4.0 paradigm guide Distribution System Operators towards a competitive market that requires the articulation of flexible demand response systems. The lack of measurement and standardization systems in the industry process chain in developing countries prevents the penetration of demand management models, generating inefficiency in the analysis and processing of information to validate the flexibility potential that large consumers can contribute to the network operator. In this sense, the research uses as input variables the energy and power of the load profile provided by the utility energy meter to obtain the disaggregated forecast in quarter-hour intervals in 4-time windows validated through metrics and its results evaluated by the RMS error to get the total error generated by the methodology with the application of Machine Learning and Big Data techniques in the Python computational tool through Combinatorial Disaggregation Optimization and Factorial Hidden Markov models. |
| first_indexed | 2024-03-13T05:01:01Z |
| format | Article |
| id | doaj.art-b9e2c1314fe1413d9101f0b66a979ff9 |
| institution | Directory Open Access Journal |
| issn | 2666-5468 |
| language | English |
| last_indexed | 2024-03-13T05:01:01Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj.art-b9e2c1314fe1413d9101f0b66a979ff92023-06-17T05:21:12ZengElsevierEnergy and AI2666-54682023-07-0113100240Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniquesMarco Toledo-Orozco0C. Celi1F. Guartan2Arturo Peralta3Carlos Álvarez-Bel4D. Morales5Institute for Energy Engineering, Universitat Politècnica de València, Camino de Vera, Valencia, 46022, Spain; Corresponding author.Electrical Engineering Career, Universidad Politécnica Salesiana, Sede Cuenca, 010103, EcuadorElectrical Engineering Career, Universidad Politécnica Salesiana, Sede Cuenca, 010103, EcuadorElectrical Engineering Career, Universidad Politécnica Salesiana, Sede Cuenca, 010103, EcuadorInstitute for Energy Engineering, Universitat Politècnica de València, Camino de Vera, Valencia, 46022, SpainElectrical Engineering Career, Circular Economy Laboratory-CIITT, Universidad Católica de Cuenca, Sede Cuenca, 010107, EcuadorTechnological advances, innovation and the new industry 4.0 paradigm guide Distribution System Operators towards a competitive market that requires the articulation of flexible demand response systems. The lack of measurement and standardization systems in the industry process chain in developing countries prevents the penetration of demand management models, generating inefficiency in the analysis and processing of information to validate the flexibility potential that large consumers can contribute to the network operator. In this sense, the research uses as input variables the energy and power of the load profile provided by the utility energy meter to obtain the disaggregated forecast in quarter-hour intervals in 4-time windows validated through metrics and its results evaluated by the RMS error to get the total error generated by the methodology with the application of Machine Learning and Big Data techniques in the Python computational tool through Combinatorial Disaggregation Optimization and Factorial Hidden Markov models.http://www.sciencedirect.com/science/article/pii/S2666546823000125Big dataCombinatorial optimizationFactorial hidden Markov modelMachine learningNon-intrusive load monitoringTime of use tariffs |
| spellingShingle | Marco Toledo-Orozco C. Celi F. Guartan Arturo Peralta Carlos Álvarez-Bel D. Morales Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniques Energy and AI Big data Combinatorial optimization Factorial hidden Markov model Machine learning Non-intrusive load monitoring Time of use tariffs |
| title | Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniques |
| title_full | Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniques |
| title_fullStr | Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniques |
| title_full_unstemmed | Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniques |
| title_short | Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniques |
| title_sort | methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non intrusive load monitoring techniques |
| topic | Big data Combinatorial optimization Factorial hidden Markov model Machine learning Non-intrusive load monitoring Time of use tariffs |
| url | http://www.sciencedirect.com/science/article/pii/S2666546823000125 |
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