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 measu...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 informa-tion 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 appli-cation of Machine Learning and Big Data techniques in the Python computational tool through Combinatorial Disaggregation Optimization and Factorial Hidden Markov models.展开更多
Nonintrusive load monitoring(NILM)is crucial for extracting patterns of electricity consumption of household appliance that can guide users9 behavior in using electricity while their privacy is respected.This study pr...Nonintrusive load monitoring(NILM)is crucial for extracting patterns of electricity consumption of household appliance that can guide users9 behavior in using electricity while their privacy is respected.This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances.It determines the number of states for each appliance using the density-based spatial clustering of applications with noise(DBSCAN)method and models the transition relationship among different states.The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model(FHMM).Thereafter,the identified states are confirmed by the verification of system states,which are the combination of the working states of individual appliances.The verification step involves comparing the total measured power consumption with the total estimated power consumption.The use of transient features can achieve fast state inference and it is suitable for online load disaggregation.The proposed method was tested on a high-resolution data set such as Labeled hlgh-Frequency daTaset for Electricity Disaggregation(LIFTED)and it outperformed other related methods in the literature.展开更多
文摘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 informa-tion 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 appli-cation of Machine Learning and Big Data techniques in the Python computational tool through Combinatorial Disaggregation Optimization and Factorial Hidden Markov models.
文摘Nonintrusive load monitoring(NILM)is crucial for extracting patterns of electricity consumption of household appliance that can guide users9 behavior in using electricity while their privacy is respected.This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances.It determines the number of states for each appliance using the density-based spatial clustering of applications with noise(DBSCAN)method and models the transition relationship among different states.The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model(FHMM).Thereafter,the identified states are confirmed by the verification of system states,which are the combination of the working states of individual appliances.The verification step involves comparing the total measured power consumption with the total estimated power consumption.The use of transient features can achieve fast state inference and it is suitable for online load disaggregation.The proposed method was tested on a high-resolution data set such as Labeled hlgh-Frequency daTaset for Electricity Disaggregation(LIFTED)and it outperformed other related methods in the literature.
