SmartHOME II

Control strategies to minimize heating energy and maximize indoor air quality and thermal comfort

Laufzeit: seit April 2010

Contrary to popular belief, the energy consumption of households is not of insignificant relevance. Based on the statistics of the German Federal Ministry of Economics and Technology, 28.9% of the total energy consumption in Germany is due to households and about 71% of this portion is used for indoor heating. Similar statistics apply to most developed countries in which about one third of all energy use can be attributed to buildings.

Heating energy can be minimized by using highly efficient thermal insulation and advanced window technology in order to significantly reduce the energy transfer between the inside and the outside of a building. However, with heat- and airtight building envelopes it is becoming increasingly difficult to keep the indoor air quality high. It is necessary to expel stale moist air from the dwelling (caused by cooking, showering and bathing) to prevent mold growth and the associated health problems, reduce odors and remove the air exhaled by occupants due to its high carbon dioxide (CO₂) concentrations. Heating (or cooling) energy escapes along with the indoor air due to the required ventilation. While ventilation with heat recovery is relatively easy to implement in buildings with central ventilation systems, it is not possible when common window ventilation is used. This project focuses on demand-controlled ventilation strategies using window ventilation since this is still the most widely applied form of ventilation in practice.

The optimal indoor heating and ventilation control strategies aim at minimizing heating energy consumption while at the same time maintaining the indoor air quality and thermal comfort at an adequate level. Not only thermal building parameters (walls, windows, roofs, etc.) and solar gains as well as internal gains (from lighting, computers, etc.), but also occupancy must be considered. For example, when the dwelling is unoccupied, the indoor temperature and ventilation rate can be lower compared to the situation in an occupied dwelling. The challenge of optimal indoor heating control is to strike the best balance between energy consumption and human thermal comfort. Surprisingly, basic on/off type or non-optimized PID controllers are still used today in most heating, ventilation and air-conditioning (HVAC) system. A growing interest has been observed to account for the specifics of the slow dynamic thermal system with usually large time delays by applying some more advanced controllers, e.g. model predictive control (MPC), fuzzy control, neutral networks, etc.

Another important fact is that human thermal comfort in an indoor environment must be met. According to e.g. ASHRAE Standard 55, general factors of human thermal comfort include air temperature, relative humidity, mean radiant temperature, drifts and ramps in operative temperature as well as airflow velocity in order to define the "comfort zone".

Indoor air quality (IAQ) is a term which refers to the air quality within and around buildings with potential influences on the health of occupants. IAQ can be mainly affected by gases (carbon dioxide, etc.) and microbial contaminants (mold, bacteria). CO₂ at high levels may cause occupants to become drowsy, get headaches, or work more inefficiently at lower activity levels. The Pettenkofer number (CO₂ concentration of 1000 ppm) is an often applied indoor air quality standard. The US National Institute for Occupational Safety and Health (NIOSH) considers that a CO₂ indoor air concentration higher than 1000 ppm is a marker that suggests inadequate ventilation.

The main goal of this project is to develop, model, evaluate and compare different strategies for heating (or cooling) and demand-controlled window ventilation (DCWV) in order to minimize the heating consumption and at the same time maximize the indoor air quality and human thermal comfort. We use our SmartHOME as research platform for these investigations. Since the thermal and ventilation parameters of different test rooms in the SmartHOME are exactly known or can be accurately measured, it is possible to build simulation models using the software packages TRNSYS/TRNFlow and ANSYS/CFX. While TRNSYS/TRNFlow can provide space-averaged, steady state results of e.g. energy consumption, escaping heat, etc. ANSYS/CFX solves the governing flow equations and can simultaneously predict airflow and heat transfer in and around buildings with high temporal resolution, which is especially important to model most interesting areas e.g. around tilted windows. By considering the thermal, air flow and pollutant transport models we obtain the detailed spatial-temporal distributions of air flow velocity, temperature and CO₂ concentration which are then compared with measurement results. For example, we already evaluated these spatial-temporal 3D/4D distributions with infiltration and natural ventilation in the large test room of the SmartHOME and are currently working on the simulations when different demand-controlled window ventilation and heating methods (floor heating, wall heating, radiator heating, etc.) are applied.

So far, we designed an optimized PID controller used for the floor heating system and iterative feedback and PID controllers for different demand-controlled window ventilation and compared the heating energy consumption considering typical weather data in Munich and different levels of CO₂-concentrations. Other control strategies are being developed and tested at current.

Heating and ventilation control system of a test room in the SmartHOME

Air temp: indoor air temperature sensor,
Inlet temp: the inlet temperature of the floor heating,
Outlet temp: the outlet temperature of the floor heating,
u_x: output signal of the controller
CO₂ %: indoor CO₂ concentration sensor,
OF: the window opening factor (here: 4 bottom-hinged windows),
OF=0: fully closed window, i.e. the opening angle is 0°,
OF=1: fully opened window, i.e. the opening angle is 90°

3D temperature distribution in the test room with floor heating (power 1448 W) and an air change rate of 6.24 1/h due to slightly tilted windows (opening factor OF = 0.044).
Depicted is the situation 5 minutes after 2 windows have been tilted when the outside temperature is 1 °C. (heat transfer coefficients: u_outwall = 0.115 W/m²K, u_window = 1.1 W/m²K, u_inwall = 0.361 W/m²K, u_floor = 0.295 W/m²K, u_ceiling = 0.279 W/m²K)