A smart electricity consumption management framework using internet of things (IoT) to optimize electricity demand

Abstract

The emerging technologies in the field of the electricity consumption are focusing the strengthening of the infrastructure of the electricity trading utilities. The infrastructure includes the smart devices which can generate real-time parameters for electricity supply monitoring in an efficient manner. This paper proposes an efficient artificial intelligence-based framework for optimal electricity demand load shifting in electricity consumption to minimize power outages in instant grid load fall and reduce the peak-to-average ratio. The framework is analyzing the acquired electricity consumption data for forecasting and load shedding at real-time which can significantly reduce customers' power outage. The proposed framework for optimal electricity demand load shifting is based on the emerging IT/OT (Information Technology/Operational Technology) technologies. We first analyzed the historical consumption of the electricity segment and then developed this framework. The analysis predicts the electricity consumption, can be utilized by the utility end as well as the consumer end by accruing economic benefits through shifting the electricity demand/load away from the very high-priced periods. The model is using various sensors and actuators in IoT Device for collecting the various real-time parameters for the electrical appliances and the machine learning algorithms to optimize the load over the electricity distribution assets. The proposed methodology improves the effectiveness of load optimization across electricity distribution assets by integrating a variety of sensors and actuators in Internet of Things devices to gather real-time metrics from electrical appliances. In addition to helping to forecast patterns in power consumption, the framework's previous consumption data analysis allows utility providers and customers to save money by strategically shifting electricity demand and load away from expensive times.

Keywords

• Optimal scheduling
• Energy management
• Artificial intelligence
• Electricity consumption
• Smart devices