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A platform for research: civil engineering, architecture and urbanism
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 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.
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 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.
Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniques
Marco Toledo-Orozco (author) / C. Celi (author) / F. Guartan (author) / Arturo Peralta (author) / Carlos Álvarez-Bel (author) / D. Morales (author)
2023
Article (Journal)
Electronic Resource
Unknown
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