Machine learning approach of detecting anomalies and forecasting time-series of IoT devices

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.

Keywords

• Anomaly detection
• Energy efficiency
• IoT
• Machine learning
• Neural networks
• Smart cities