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Title	Energy Consumption Outlier Detection With Ai Models In Modern Cities: A Case Study From North-Eastern Mexico
ID_Doc	23175
Authors	Solís-Villarreal J.-A.; Soto-Mendoza V.; Navarro-Acosta J.A.; Ruiz-y-Ruiz E.
Year	2024
Published	Algorithms, 17, 8
DOI	http://dx.doi.org/10.3390/a17080322
Abstract	The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; therefore; anomaly detection in electricity consumption predictions has become an important research topic. This work focuses on the study of the detection of anomalies in domestic electrical consumption in Mexico. A predictive machine learning model of future electricity consumption was generated to evaluate various anomaly-detection techniques. Their effectiveness in identifying outliers was determined, and their performance was documented. A 30-day forecast of electrical consumption and an anomaly-detection model have been developed using isolation forest. Isolation forest successfully captured up to 75% of the anomalies. Finally, the Shapley values have been used to generate an explanation of the results of a model capable of detecting anomalous data for the Mexican context. © 2024 by the authors.
Author Keywords	artificial intelligence; data-driven approach; energy efficiency; household energy consumption; outlier detection; smart cities

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