Methodology for the disaggregation and forecast of demand flexibility in large consumers with the application of non-intrusive load monitoring techniques
作者机构：Institute for Energy EngineeringUniversitat Politecnica de Val`enciaCamino de VeraValencia46022Spain Electrical Engineering CareerUniversidad Politecnica SalesianaSede Cuenca010103Ecuador Electrical Engineering CareerCircular Economy Laboratory-CIITTUniversidad Catolica de CuencaSede Cuenca010107Ecuador
出 版 物：《Energy and AI》 (能源与人工智能（英文）)
年 卷 期：2023年第13卷第3期
页 面：88-103页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：Universidad Politécnica Salesiana del Ecuador, UPS Universidad Católica de Cuenca, UCACUE Universitat Politècnica de València, UPV
主 题：Big data Combinatorial optimization Factorial hidden Markov model Machine learning Non-intrusive load monitoring Time of use tariffs
摘 要：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 *** 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 *** 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.
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