Smart City Gnosys

Smart city article details

Title	Machine Learning Approach Of Detecting Anomalies And Forecasting Time-Series Of Iot Devices
ID_Doc	35909
Authors	Malki A.; Atlam E.-S.; Gad I.
Year	2022
Published	Alexandria Engineering Journal, 61, 11
DOI	http://dx.doi.org/10.1016/j.aej.2022.02.038
Abstract	With the development of smart cities infrastructure, these cities’ energy efficiency has become a major problem. Many public buildings, including health centers, educational institutions, and other institutions, consume more energy. It is of great significance that we produce as much energy as possible to be close to the real energy demand. IoT systems have many benefits in the smart city, such as controlling and reducing energy consumption. Therefore, governments can save a lot of money. In the current study, IoT sensors are used to collect data from home appliances. There was a particular trend in the amount of energy consumed by each home appliance. Moreover, we investigate the potential to integrate machine learning-based anomaly detection approaches to improve the maintenance of the power systems and control as a fundamental part of the smart city concept. The models were selected to catch trends and changes in energy consumption at an early stage. Based on the results, the Prophet and LightGBM models outperform vector autoregressive (VAR) models in point anomaly detection. Furthermore, the Prophet and LightGBM models predict how much energy will be used in the future based on weather and time information. © 2022
Author Keywords	Anomaly detection; Energy efficiency; IoT; Machine learning; Neural networks; Smart cities; Time series analysis

Similar Articles

Id	Similarity	Authors	Title	Published
9623	0.925	Galeb B.; Saad H.; Bashar H.; Al-Majdi K.; Al-Hilali A.	Anomaly Detection In Smart Home Electrical Appliances Using Machine Learning With Statistical Algorithms And Optimized Time Series Algorithms	Journal of Mechanics of Continua and Mathematical Sciences, 19, 5 (2024)
19180	0.906	Mund C.; Altherr L.C.	Detecting Anomalous Energy Consumptions In Smart Buildings - An Overview Of Two Unsupervised Techniques	Proceedings - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022 (2022)
43509	0.89	Hasani Z.; Krrabaj S.; Krasniqi M.	Proposed Model For Real-Time Anomaly Detection In Big Iot Sensor Data For Smart City	International Journal of Interactive Mobile Technologies, 18, 3 (2024)
23175	0.877	Solís-Villarreal J.-A.; Soto-Mendoza V.; Navarro-Acosta J.A.; Ruiz-y-Ruiz E.	Energy Consumption Outlier Detection With Ai Models In Modern Cities: A Case Study From North-Eastern Mexico	Algorithms, 17, 8 (2024)
36050	0.873	Samikwa E.; Schärer J.; Braun T.; Di Maio A.	Machine Learning-Based Energy Optimisation In Smart City Internet Of Things	Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) (2023)
35996	0.873	Haque A.; Malik A.	Machine Learning In Renewable Energy Systems For Smart Cities	Smart Cities: Power Electronics, Renewable Energy, and Internet of Things (2024)
19250	0.872	Śmiałkowski T.; Czyżewski A.	Detection Of Anomalies In The Operation Of A Road Lighting System Based On Data From Smart Electricity Meters	Energies, 15, 24 (2022)
36066	0.87	Ajagunsegun T.; Li J.; Bamisile O.; Ohakwe C.	Machine Learning-Based System For Managing Energy Efficiency Of Public Buildings: An Approach Towards Smart Cities	2022 4th Asia Energy and Electrical Engineering Symposium, AEEES 2022 (2022)
2981	0.869	Villarreal R.; Chamorro-Solano S.; Vega-Sampayo Y.; Espejo C.A.; Cantillo S.; Gaviria L.; Paez J.; Ochoa C.; Moreno S.; Polo C.; Pestana-Nobles R.; Montoya C.	A New Approach To Electrical Fault Detection In Urban Structures Using Dynamic Programming And Optimized Support Vector Machines	Sensors, 25, 7 (2025)
32444	0.868	Nijim M.; Kanumuri V.; Albetaineh H.; Goyal A.	Intelligent Monitoring And Management Of Smart Buildings Using Machine Learning: Optimizing User Behavior And Energy Efficiency	Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 (2023)