Intelligent Home Energy Management System based on Bi-directional Long-short Term Memory and Reinforcement Learning

The dynamic nature of the electricity market need an efficient energy management and control system to take perfect decisions accordingly. House hold appliances is the contemporary study being adopted to improve the performance and balance the fluctuation between power system and smart home. This article proposes an intelligent home energy management system (IHEMS) incorporated with a prediction model and optimization model. To address the uncertainty of future energy load and its cost, a suitable prediction model based on Bi-directional long short Term memory (Bi-LSTM) is contributed. In collaboration with the prediction model, an optimization model based on reinforcement learning is presented to schedule the home appliances by taking optimal decisions. To validate the performance of the proposed scheme, Intensive simulation is performed with adoptable, un-adoptable and manageable loads of household appliances. The results confirm that the proposed scheme address the problem of energy management for numerous appliances, reduce the total energy consumption with total energy bill and minimize the user comfort level.

Automated summary

This article proposes an intelligent home energy management system (IHEMS) with a prediction model based on Bi-directional long short Term memory (Bi-LSTM) and an optimization model based on reinforcement learning to address the uncertainty of future energy load and its cost.