Energy Consumption Scheduling of HVAC Considering Weather Forecast Error Through the Distributionally Robust Approach

Y. F. Du, L. Jiang, Chao Duan*, Y. Z. Li, J. S. Smith

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

In this paper, the distributionally robust optimization approach (DROA) is proposed to schedule the energy consumption of the heating, ventilation and air conditioning (HVAC) system with consideration of the weather forecast error. The maximum interval of the outdoor temperature is partitioned into subintervals, and the proposed DROA constructs the ambiguity set of the probability distribution of the outdoor temperature based on the probabilistic information of these subintervals of historical weather data. The actual energy consumption will be adjusted according to the forecast error and the scheduled consumption in real time. The energy consumption scheduling of HVAC through the proposed DROA is formulated as a nonlinear problem with distributionally robust chance constraints. These constraints are reformulated to be linear and then the problem is solved via linear programming. Compared with the method that takes into account the weather forecast error based on the mean and the variance of historical data, simulation results demonstrate that the proposed DROA effectively reduces the electricity cost with less computation time, and the electricity cost is reduced compared with the traditional robust method.

Original languageEnglish (US)
Pages (from-to)846-857
Number of pages12
JournalIEEE Transactions on Industrial Informatics
Volume14
Issue number3
DOIs
StatePublished - Mar 2018

Keywords

  • Demand response (DR)
  • distributionally robust optimization
  • energy consumption scheduling
  • heating
  • ventilation and air conditioning (HVAC)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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