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 pre-dict how much energy will be used in the future based on weather and time information.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).