Extracting interpretable building control rules from multi-objective model predictive control data sets

Developing intelligent building control strategies is increasingly becoming a multi-objective problem as owners, occupants, and operators seek to balance performance across energy, operating expense, environmental concerns, indoor environmental quality, and electric grid incentives. Implementing multi-objective optimal controls in buildings is challenging and often not tractable due to the complexity of the problem and the computational burden that frequently accompanies such optimization problems. In this work, we extract near-optimal rule sets from a database of non-dominated solutions, created by applying multi-objective model predictive control to detailed EnergyPlus models. We first apply multi-criteria decision analysis to rank the non-dominated solutions and select a subset of consistent and plausible operating strategies that can satisfy operator or occupant preferences. Next, unsupervised clustering is applied to highlight recurring control patterns. In the final step, we build a supervised classification model to identify the right optimal temperature control patterns for a particular day. The performance of the simplified rule sets is then quantified through simulation. Despite the dramatically simpler form, the best rule sets were able to achieve 95–97% of the energy savings and 89–92% of the cost objective savings of the fully detailed model predictive controller, while achieving similar thermal comfort and peak electrical demand.

© This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

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Work Title Extracting interpretable building control rules from multi-objective model predictive control data sets
Access
Open Access
Creators
  1. Min Gyung Yu
  2. Gregory S. Pavlak
Keyword
  1. Clustering
  2. Classification
  3. Multi-objective Optimization
  4. Rule extraction
  5. Model predictive control of buildings
License CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
Work Type Article
Publisher
  1. Elsevier BV
Publication Date February 2022
Publisher Identifier (DOI)
  1. 10.1016/j.energy.2021.122691
Source
  1. Energy
Deposited June 15, 2022

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  • Created
  • Added Extracting_simplified_control_rules_from_multi_objective_datasets_accepted-1.pdf
  • Added Creator Min Gyung Yu
  • Added Creator Gregory S. Pavlak
  • Published
  • Updated Work Title, Keyword Show Changes
    Work Title
    • Extracting interpretable building control rules from multi-objective model predictive control data sets
    • ! Extracting interpretable building control rules from multi-objective model predictive control data sets
    Keyword
    • Clustering, Classification, Multi-objective Optimization, Rule extraction, Model predictive control of buildings
  • Updated Creator Gregory S. Pavlak
  • Updated Work Title Show Changes
    Work Title
    • ! Extracting interpretable building control rules from multi-objective model predictive control data sets
    • Extracting interpretable building control rules from multi-objective model predictive control data sets
  • Updated Creator Gregory S. Pavlak
  • Updated