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A combined genetic optimization with AdaBoost ensemble model for anomaly detection in buildings electricity consumption
https://orcid.org/0000-0002-7160-8713
https://orcid.org/0000-0002-6486-0128
Highlights: The Adaboost ensemble learning model is applied to the field of anomaly detection in buildings electricity consumption. Much time and energy are taken in data processing and feature extraction, which emphasises the importance of data-feature mining throughout the process and ensures the high quality and reliability of data. The process is proposed to deal with the electricity theft detection problem in buildings from multiple perspectives (e.g. data pre-processing, feature extraction, algorithm improvement, and hyperparameter optimisation).
Abstract Buildings account for a significant portion of the world’s electricity consumption, including industrial, commercial, residential, hospital buildings, etc. During building operation, significant energy consumption and economic loss may be caused due to various forms of electricity theft. To this end, an ensemble model for electricity theft detection method based on genetic optimization is developed. At first, synthetic samples that have an approximate distribution to that of true samples of electricity theft are prepared through use of a synthetic minority oversampling technique (SMOTE). Afterwards, features of anomalous electricity consumption are extracted with the aid of dimension reduction through principal component analysis (PCA). Finally, an ensemble deep learning network based on AdaBoost is established to mine implicit information in continuous time series data and detect anomalous electricity consumption of labelled users. Moreover, the hyperparameters of the deep neural network are optimized based on a genetic algorithm (GA). By taking data pertaining to the electricity consumption of users collected from smart meters of the State Grid Corporation of China as examples, the results show that our model is superior to other detection methods (such as support vector machine (SVM), random forest (RF), and traditional artificial neural network (ANN)) in the sensitivity and the area under the curve (AUC).
A combined genetic optimization with AdaBoost ensemble model for anomaly detection in buildings electricity consumption
https://orcid.org/0000-0002-7160-8713
https://orcid.org/0000-0002-6486-0128
Highlights: The Adaboost ensemble learning model is applied to the field of anomaly detection in buildings electricity consumption. Much time and energy are taken in data processing and feature extraction, which emphasises the importance of data-feature mining throughout the process and ensures the high quality and reliability of data. The process is proposed to deal with the electricity theft detection problem in buildings from multiple perspectives (e.g. data pre-processing, feature extraction, algorithm improvement, and hyperparameter optimisation).
Abstract Buildings account for a significant portion of the world’s electricity consumption, including industrial, commercial, residential, hospital buildings, etc. During building operation, significant energy consumption and economic loss may be caused due to various forms of electricity theft. To this end, an ensemble model for electricity theft detection method based on genetic optimization is developed. At first, synthetic samples that have an approximate distribution to that of true samples of electricity theft are prepared through use of a synthetic minority oversampling technique (SMOTE). Afterwards, features of anomalous electricity consumption are extracted with the aid of dimension reduction through principal component analysis (PCA). Finally, an ensemble deep learning network based on AdaBoost is established to mine implicit information in continuous time series data and detect anomalous electricity consumption of labelled users. Moreover, the hyperparameters of the deep neural network are optimized based on a genetic algorithm (GA). By taking data pertaining to the electricity consumption of users collected from smart meters of the State Grid Corporation of China as examples, the results show that our model is superior to other detection methods (such as support vector machine (SVM), random forest (RF), and traditional artificial neural network (ANN)) in the sensitivity and the area under the curve (AUC).
A combined genetic optimization with AdaBoost ensemble model for anomaly detection in buildings electricity consumption
https://orcid.org/0000-0002-7160-8713
https://orcid.org/0000-0002-6486-0128
Highlights: The Adaboost ensemble learning model is applied to the field of anomaly detection in buildings electricity consumption. Much time and energy are taken in data processing and feature extraction, which emphasises the importance of data-feature mining throughout the process and ensures the high quality and reliability of data. The process is proposed to deal with the electricity theft detection problem in buildings from multiple perspectives (e.g. data pre-processing, feature extraction, algorithm improvement, and hyperparameter optimisation).
Abstract Buildings account for a significant portion of the world’s electricity consumption, including industrial, commercial, residential, hospital buildings, etc. During building operation, significant energy consumption and economic loss may be caused due to various forms of electricity theft. To this end, an ensemble model for electricity theft detection method based on genetic optimization is developed. At first, synthetic samples that have an approximate distribution to that of true samples of electricity theft are prepared through use of a synthetic minority oversampling technique (SMOTE). Afterwards, features of anomalous electricity consumption are extracted with the aid of dimension reduction through principal component analysis (PCA). Finally, an ensemble deep learning network based on AdaBoost is established to mine implicit information in continuous time series data and detect anomalous electricity consumption of labelled users. Moreover, the hyperparameters of the deep neural network are optimized based on a genetic algorithm (GA). By taking data pertaining to the electricity consumption of users collected from smart meters of the State Grid Corporation of China as examples, the results show that our model is superior to other detection methods (such as support vector machine (SVM), random forest (RF), and traditional artificial neural network (ANN)) in the sensitivity and the area under the curve (AUC).
A combined genetic optimization with AdaBoost ensemble model for anomaly detection in buildings electricity consumption
Qu, Zhijian (author) / Liu, Hanxin (author) / Wang, Zixiao (author) / Xu, Juan (author) / Zhang, Pei (author) / Zeng, Han (author)
Energy and Buildings ; 248
2021-06-11
Article (Journal)
Electronic Resource
English
| Elsevier | 2025
Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption
| DOAJ | 2021