Desigo Energy-efficient applications: Predictive and self-adapting heating controller Application data sheet Answers for infrastructure.
Desigo Energy-efficient applications: Predictive and selfadapting heating controller Application data sheet CM110745en_02 01.03.2012 Subject to technical change.
Table of contents 1 Introduction to predictive heating controller .......................................5 2 Basics.......................................................................................................6 2.1 Heating system .........................................................................................6 2.2 Conventional heating controllers...............................................................6 3 Predictive heating controller ........................................
1 Introduction to predictive heating controller The predictive heating controller application allows for controlling a heating group with mixing circuit control, pump, and modulating valve. To this end, the controller calculates the optimum flow temperature setpoint based on building model and outside temperature forecast. As an option, the controller independently adapts the building model to the building based on measured data.
2 Basics 2.1 Heating system All buildings interact dynamically with the environment. This thermal interaction can be determined and described with the aid of energy balance and heat transport equations. They form the basis for heating system and associated heat output design. A heating system aims at providing the required thermal comfort at the lowest possible energy consumption.
3 Predictive heating controller 3.1 Overview Predictive heating control considers a building's total performance and controls the plant to achieve the required comfort while lowering energy consumption. To do this, predictive heating control uses a building model determining the future (optimized) flow temperature setpoint profile by means of numeric optimization. This process includes outside temperature forecasting as well as the future course of the room temperature setpoint.
3.2 The principal functions 3.2.
Case 1 Internal calculation of outside temperature forecasts: An outside temperature forecast is generated based on past outside temperatures. A persistency forecast is used to this end. This means that the measured course of the past 24 hours will be repeated within the next 24 hours. At the start of the forecast, however, a correction using the currently measured outside temperature value is made. The calculation is carried out as part of the function for the next 64 hours.
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4 Energy savings The following comparison between predictive heating control and two reference controls is based on annual simulations. The controls are set similarly in all cases. The results apply to the case at hand using the key data. The simulated building has a fairly high heat demand due to relatively poor heat insulation and low assumed heat gains. Key data for the simulated office building Building – Swiss average with regard to heat insulation 2 – U-value for building shell is 1.
Pump runtime zone pump in [h] per year (8760 h) Comfort: Lower deviation from room temperature setpoint in [Kh] Reference B Heating energy consumption: Useful energy in [kWh/m2] Reference A Predictive heating control Simulation results 151 144 (-4 %) 174 (+13 %) 3'081 3'077 (0%) 6'237 (+102%) 310 2'331 (+652%) 427 (+38%) Table 4-1: Overview of simulation results Reference A: Economy mode refers to the period when the room temperature is set back compared to the Comfort temperature.
Conclusion When considering both heating energy consumption and comfort, the simulated example allows for comparable comfort at min. 8 to 13% energy savings for properly set controls. In addition, predictive heating control achieves excellent comfort values compared to reference plants, thus contributing considerably to greater comfort.
5 Advantages and customer benefits 5.1 Advantages – – – – – – – Integrated outside temperature forecast Optional use of an external outside temperature forecast (e.g. from a meteorological service). Switch-on/switch-off optimization Adaptation of building model parameters, including heating curve adaptation Optimize flow temperature setpoint for min.
6 Field of use Predictive heating control is a component of a heating circuit with flow temperature control. The application is ideal for fast-reacting heat delivery systems such as: – – – – Radiators Convectors Ceiling heating Fan coils The application can be used on both new and existing plants. As a result, no additional plant components are required.
9 Field devices No special requirements are placed on field devices with regard to measuring precision, quality, etc. Siemens field devices should be used whenever possible. 10 Versioning The application can be used as of Desigo V4.0.
11 Appendix 11.1 Plant components The predictive heating control application is modular in design with variants and options to adapt the application to the widest possible range of heating groups with mixed circuit control. It provides solid coverage of typical ranges on both distribution as well as reference room.
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