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

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

Research output: Contribution to conferencePaper


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 pages8
StatePublished - 2019
EventPower and Energy Society (PES) General Meeting - Portland, Oregon
Duration: 5 Aug 201810 Aug 2018


ConferencePower and Energy Society (PES) General Meeting
CityPortland, Oregon

Bibliographical note

See NREL/CP-5D00-73329 for paper as published in IEEE proceedings

NREL Publication Number

  • NREL/CP-5D00-70422


  • approximate dynamic programming
  • demand response
  • electric vehicle
  • home energy management system
  • stochastic mixed-integer programming


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