Prediction Method of Electric Energy Metering Device Based on Software-Defined Networking

Abstract

The energy storage or management system is becoming more important as electrical energy demand globally grows. In the first step of the energy management plan, energy forecasting is an essential element. Software-defined network (SDN) seeks to change network architecture and processes to be agile and effectively enhance the functionality of fundamental system components such as switches and routers. Predicting electric energy is crucial to support demand-side management in the building business. The total power load of the individual building services systems management is becoming a challenging process to be predicted for buildings with electricity metering systems. Conventional model energy prediction focuses on predictive accuracy, and energy consumption must be predicted according to different conditions to build an efficient system. Novel deep learning-based smart energy metering prediction (DL-SEMP) technique is considered to overthrow such accuracy lack in prediction. Blockchain-based SDN has emerged a new architect for obtaining a distributed network in blockchain technology.