TY - JOUR
T1 - Enhancing anomaly detection in electrical consumption profiles through computational intelligence
AU - Luna-Romero, Santiago Felipe
AU - Serrano-Guerrero, Xavier
AU - de Souza, Mauren Abreu
AU - Escrivá-Escrivà, Guillermo
N1 - Publisher Copyright:
© 2023
PY - 2024/6
Y1 - 2024/6
N2 - The advancement of society and the raising of people's standards of living depend heavily on electricity in today's world. The “zero energy buildings” idea, which recommends that buildings become self-sufficient in renewable energy to prevent the emission of CO2 into the environment, is now one of the most significant initiatives connected to building energy efficiency. This article describes a computational intelligence method to detect anomalous variations in a facility's energy use and infer a potential cause of such changes. The model is built using five sets of historical power consumption data from three buildings spread across four nations (Ecuador, Spain, France, and Canada), which are categorized based on the anomaly type each piece of data represents. Through a statistical study of the confidence interval, the proposed method, first determines the consumption patterns for each day of the week in each of the building's data sets. After normalizing the day to be studied toward its “Z” value, it is then cataloged using a machine learning model. The proposed method is evaluated in comparison to a purely statistical method called SAEEC methodology and it is discovered that the proposed method offers a relative improvement in accuracy, false positive rate (FPR), and false negative rate (FNR) of 12.41%, 42, 36%, and 42.45%, respectively, for the detection of atypical values in electrical energy consumption.
AB - The advancement of society and the raising of people's standards of living depend heavily on electricity in today's world. The “zero energy buildings” idea, which recommends that buildings become self-sufficient in renewable energy to prevent the emission of CO2 into the environment, is now one of the most significant initiatives connected to building energy efficiency. This article describes a computational intelligence method to detect anomalous variations in a facility's energy use and infer a potential cause of such changes. The model is built using five sets of historical power consumption data from three buildings spread across four nations (Ecuador, Spain, France, and Canada), which are categorized based on the anomaly type each piece of data represents. Through a statistical study of the confidence interval, the proposed method, first determines the consumption patterns for each day of the week in each of the building's data sets. After normalizing the day to be studied toward its “Z” value, it is then cataloged using a machine learning model. The proposed method is evaluated in comparison to a purely statistical method called SAEEC methodology and it is discovered that the proposed method offers a relative improvement in accuracy, false positive rate (FPR), and false negative rate (FNR) of 12.41%, 42, 36%, and 42.45%, respectively, for the detection of atypical values in electrical energy consumption.
KW - Anomaly detection
KW - Building energy efficiency
KW - Computational intelligence method
KW - Electrical energy consumption
KW - Energy consumption patterns
KW - Zero energy buildings
UR - http://www.scopus.com/inward/record.url?scp=85181122065&partnerID=8YFLogxK
U2 - 10.1016/j.egyr.2023.12.045
DO - 10.1016/j.egyr.2023.12.045
M3 - Article
AN - SCOPUS:85181122065
SN - 2352-4847
VL - 11
SP - 951
EP - 962
JO - Energy Reports
JF - Energy Reports
ER -