MPC-Trained ANFIS for Control of MicroCSP Integrated into a Building HVAC System

Mohamed Toub1, Mahdi Shahbakhti2, Rush Robinett3, Ghassane Aniba4

  • 1Ecole Mohammadia d'Ingénieurs
  • 2Michigan Technological University
  • 3Michigan Tech University
  • 4Mohammadia School of Engineers

Details

11:00 - 11:20 | Wed 10 Jul | Franklin 7 | WeA07.4

Session: Control & Energy Management of Building Systems

Abstract

This paper presents the design of an easily implementable rule-based controller that can minimize the electrical energy consumption of a building heating, ventilation, and air-conditioning (HVAC) system integrated with a micro-scale concentrated solar power (MicroCSP) system. A model predictive control (MPC) scheme is developed to optimize MicroCSP electrical and thermal energy flows for HVAC use in a building. Despite its attractiveness regarding energy savings and thermal comfort satisfaction, MPC requires high computational resources and can not be easily implemented on the common low-cost HVAC controllers available in the market. To cope with these issues, two MPC-trained adaptive neuro-fuzzy inference system (ANFIS) models are designed to control the building HVAC with MicroCSP. Simulation results exploiting real operation data from an office building at Michigan Technological University and our newly purchased MicroCSP are presented. It is shown that the resulting controller can reproduce the MPC reasoning and performance while being simpler and much more computationally efficient.