Distributed Model Predictive Control for Energy and Comfort Optimization in Large Buildings Using Piecewise Affine Approximation

TL;DR

This paper introduces a distributed control scheme using Piecewise Affine approximation and convex ADMM to optimize energy and comfort in large buildings, significantly reducing computation time.

Contribution

It presents a novel PWA-based distributed MPC approach with a convex ADMM algorithm for large-scale nonlinear building control problems.

Findings

Reduces computation time by 86% compared to centralized control

Successfully handles nonlinear components with PWA approximation

Converges to a local optimum efficiently

Abstract

The control of large buildings encounters challenges in computational efficiency due to their size and nonlinear components. To address these issues, this paper proposes a Piecewise Affine (PWA)-based distributed scheme for Model Predictive Control (MPC) that optimizes energy and comfort through PWA-based quadratic programming. We utilize the Alternating Direction Method of Multipliers (ADMM) for effective decomposition and apply the PWA technique to handle the nonlinear components. To solve the resulting large-scale nonconvex problems, the paper introduces a convex ADMM algorithm that transforms the nonconvex problem into a series of smaller convex problems, significantly enhancing computational efficiency. Furthermore, we demonstrate that the convex ADMM algorithm converges to a local optimum of the original problem. A case study involving 36 zones validates the effectiveness of the proposed method. Our proposed method reduces execution time by 86\% compared to the centralized version.