ADP-Based Home Energy Management System: A Case Study Using DYNAMO

Bryan Palmintier, Dheepak Krishnamurthy, Mohammad Faqiry, Li Wang, Hongyu Wu

Research output: Contribution to conferencePaperpeer-review

6 Scopus Citations


This paper explores Home Energy Management System (HEMS) algorithms to minimize household cost while maintaining comfort when faced with uncertain weather, and demand. Specifically, we consider a HEMS that optimizes forward looking schedules for a home's heating, ventilation, and air conditioning (HVAC); water heater (WH); and electric vehicle (EV) charging while considering uncertainty in outside temperature, hot water usage, and non-controllable load (NCL). We adopt a Dynamic Programming (DP) formulation and utilize the Dynamic programming for Adaptive Modeling and Optimization (DYNAMO) toolkit to implement DP and approximate dynamic programming (ADP) algorithms. Simulation results under a single tariff plan compare the quality of the solution generated by ADP to that of DP, and show significant improvement in computation time while maintaining acceptable solution accuracy.

Original languageAmerican English
Number of pages5
StatePublished - 21 Dec 2018
Event2018 IEEE Power and Energy Society General Meeting, PESGM 2018 - Portland, United States
Duration: 5 Aug 201810 Aug 2018


Conference2018 IEEE Power and Energy Society General Meeting, PESGM 2018
Country/TerritoryUnited States

Bibliographical note

See NREL/CP-5D00-70422 for preprint

NREL Publication Number

  • NREL/CP-5D00-73329


  • Approximate dynamic programming
  • Demand response
  • Electric vehicle
  • Home energy management system
  • Stochastic mixed-integer programming


Dive into the research topics of 'ADP-Based Home Energy Management System: A Case Study Using DYNAMO'. Together they form a unique fingerprint.

Cite this