A Fast and Optimal Smart Home Energy Management System: State-Space Approximate Dynamic Programming

Zhiheng Zhao,Chanaka Keerthisinghe

doi:10.1109/access.2020.3023665

Abstract

Dynamic programming (DP) can be used to generate the optimal schedules of a smart home energy management system (SHEMS), however, it is computationally difficult because we have to loop over all the possible states, decisions and outcomes. This paper proposes a novel state-space approximate dynamic programming (SS-ADP) approach to quickly solve a SHEMS problem but with similar solutions as DP. The state-space approximations are made using a hierarchical approach, which involves clustering and machine learning. The proposed SS-ADP can generate the day-ahead value functions quickly without compromising the solution quality because it only loops over the necessary state-space. Our simulation results showed that the solutions from the SS-ADP approach are within 0.8% of the optimal DP solutions but saves the computational time by at least 20%. The paper also presents a fast real-time control strategy under uncertainty using the Bellman optimality condition and long short-term memory recurrent neural networks (LSTM-RNN). The Bellman equation uses the day-ahead value function from the SS-ADP and the instantaneous contribution function to make fast real-time decisions. The instantaneous contribution is calculated using the PV and load predicted using LSTM-RNN, which performs significantly better than the widely used persistence method.

Highlights

In order to maximize the benefits of PV-storage systems, residential energy users will use a smart home energy management system (SHEMS) to schedule their energy use
Implementing a SHEMS in a cloud would mean that the computational speed would become less important, since the computational time of Dynamic programming (DP) increases exponentially when we increase the number of devices [16], it would be beneficial either way to keep the computational cost to a minimum level
We first propose a hierarchical approach for state-space approximate dynamic programming (SS-ADP) to generate the value functions quickly but without affecting the solution quality

Summary

INTRODUCTION

Smart home is an automated residential building that uses distributed energy resources for managing energy consumption and providing suitable levels of comfort to it’s inhabitants. On the other hand DP and ADP generate value functions and value function approximations, respectively, in the day-ahead planning stage, which lets the user make fast real-time decisions using Bellman optimality condition, which is a much faster process compared to having to solve a difficult optimization problem. Implementing a SHEMS in a cloud would mean that the computational speed would become less important, since the computational time of DP increases exponentially when we increase the number of devices [16], it would be beneficial either way to keep the computational cost to a minimum level Given these insights, we first propose a hierarchical approach for state-space approximate dynamic programming (SS-ADP) to generate the value functions quickly but without affecting the solution quality.

SMART HOME ENERGY MANAGEMENT PROBLEM

INSTANTIATION

MODELING

RESULTS

Findings

VIII. CONCLUSION