Learning indoor temperature predictions for optimal load ensemble control

Nikolina Čović, Hrvoje Pandžić, Yury Dvorkin

Research output: Contribution to journalArticlepeer-review


Aggregation of electrical appliances in residential households is a potent source for harnessing demand-side flexibility that can be leveraged by utilities or demand response aggregators for various transmission- and distribution-level services. However, the aggregated flexibility of these resources depends on such external factors as behavioral preferences of electricity consumers and temperature. More importantly, these external factors can be interdependent, e.g. ensuring the comfort of electricity consumers requires maintaining in-door temperatures within a certain range. This paper develops a deep learning approach for in-door temperature predictions and then integrates it with optimal load ensemble control. To improve the accuracy of deep learning, which is notorious for a lack of physical interpretability and performance guarantees, we employ the concept of physics-informed neural networks, which allows for incorporating a physical (thermal) building model. We use a real-world National Institute of Standards and Technology (NIST) data set to demonstrate the usefulness of temperature learning for such demand response application.

Original languageEnglish (US)
Article number108384
JournalElectric Power Systems Research
StatePublished - Oct 2022


  • Markov decision process
  • Physics-informed machine learning
  • Smart buildings

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


Dive into the research topics of 'Learning indoor temperature predictions for optimal load ensemble control'. Together they form a unique fingerprint.

Cite this