Model Predictive Control and energy optimisation in residential building with electric underfloor heating system

被引:19
作者
Lawrynczuk, Maciej [1 ]
Oclon, Pawel [2 ]
机构
[1] Warsaw Univ Technol, Fac Elect & Informat Technol, Inst Control & Computat Engn, Ul Nowowiejska 15-19, PL-00665 Warsaw, Poland
[2] Cracow Univ Technol, Fac Mech Engn, Inst Thermal Power Engn, Al Jana Pawla II 37, PL-31864 Krakow, Poland
关键词
Temperature control; Residential building; Electric underfloor heating; Energy optimisation; Model predictive control; Model identification; WEATHER FORECAST; THERMAL COMFORT; NEURAL-NETWORK; FLOOR; PERFORMANCE; EFFICIENT; STORAGE; TEMPERATURE; SIMULATION; MANAGEMENT;
D O I
10.1016/j.energy.2019.06.062
中图分类号
O414.1 [热力学];
学科分类号
摘要
This work is concerned with modelling, Model Predictive Control (MPC) and on-line energy optimisation of a residential building in which an underfloor heating system based on electric heating foils is used. At first, the first-principle model is developed to determine air temperature inside the building. Low-order linear models for MPC are found. Tuning of MPC is discussed, both set-point change and disturbance compensation cases are discussed. Next, two control system structures are considered: MPC with a constant temperature set-point and MPC cooperating with a set-point optimiser which repeatedly calculates on-line the set-point in order to minimise the amount of energy used. Effectiveness of these structures is validated in simulations using a real scenario of outdoor temperature changes. In the first structure a natural method to reduce energy used is to slightly reduce the temperature set-point, e.g. reduction of the set-point from 21 degrees C to 18.5 degrees C leads to reducing the energy usage by approx. 1430 KWh during the heating season. Introduction of the set-point optimiser makes it possible to further reduce energy used by approx. 300 KWh annually. The presented solution is computationally efficient since in MPC and set-point optimisation the classical quadratic optimisation method is used on-line. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1028 / 1044
页数:17
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