Calibration of a Building Energy Simulation Model to BMS Data Using an Analytical Optimisation Approach Daniel Coakley, Dr. Paul Raftery & Dr. Padraig Molloy Dept. of Mechanical & Biomedical Engineering, NUI, Galway Abstract
Methodology
The built environment accounts for approximately 40% of energy consumption and is responsible for 30-40% of greenhouse gas emissions [1]. Under the Kyoto protocol, developed countries are bound to an agreement to reduce greenhouse gas (GHG) emissions 5% below 1990 levels by the period 2008-2012. In order to achieve this goal, governments are promoting the development of new low-energy buildings and retrofitting of existing buildings for increased energy efficiency.
1. A Building Energy Simulation (BES) model of the library building will be developed.
This project aims to reduce the energy requirement for space heating in buildings through the implementation of calibrated building energy simulation models to develop an approach for optimising building control.
Introduction Whole building energy modelling tools, such as EnergyPlus, provide a means of understanding building operation as well as optimising performance. However, due to the complexity of the built environment and prevalence of large numbers of independent interacting variables, it is difficult to achieve an accurate representation of real-world building operation. EnergyPlus® dynamic energy simulation package developed by Lawrence Berkeley National Laboratory with support from the U.S Department of Energy. It is capable of modeling building heating, cooling, lighting, ventilating and other energy flows
By ‘calibrating’ the model to actual measured data, we can achieve more accurate and reliable results. A review of current literature on this topic has revealed that there is no generally accepted method by which building energy models should be calibrated.
2. Data pertaining to the building construction, systems and operating schedules will be acquired. The model will be developed based on this evidence and will be tracked using version control software. 3. Subsequently this BES model will be calibrated using the proposed analytical methodology. a) Firstly, this will involve reducing the dimensionality of the parameter space by performing a sensitivity analysis to determine influential parameters and reasonable ranges for parameter values. b) Secondly, a two-stage MC simulation process will be used to find a realistic set of solutions that satisfy the objective function. c) Finally, by isolating controllable parameters identified in the initial optimisation approach, it is proposed that the calibrated model will be used to assist in the identification of optimal operational and control strategies for the building.
WEATHER STATION Dry-Bulb Temperature Relative Humidity Barometric Pressure Wind Speed Wind Direction Rainfall Solar Irradiance
BUILDING MANAGEMENT SYSTEM (BMS) Heating Load (kWh) Electrical Load (kWh) Zone Temperature Zone Carbon Dioxide
BUILDING AUDIT Occupancy Schedules Equipment Schedules System and Geometry Verification Additional Building Details
STATIC KNOWLEDGE OF BUILDING & SYSTEM TEMPLATES, PREFERRED VALUES AND RANGES
MID-POINT LATIN HYPERCUBE MONTE CARLO GENERATES SET OF NUMEROUS TRIALS OF PARAMETER-VECTORS
CONSTRUCT BUILDING ENERGY SIMULATION (BES) MODEL
RUN BUILDING ENERGY SIMULATION PROGRAM
PERFORM REGIONAL SENSITIVITY ANALYSIS
Optional
DYNAMIC HEURISTIC CALIBRATION TRADITIONAL MATHEMATICAL OPTIMISATION
RUN SIMULATION PROGRAM
Aims
An existing 700m2 library (see below) will be used as a case study.
Recommended
BLIND COARSE BOUNDED GRID SEARCH
VERSION CONTROL Changes to model are tracked using version control software and referenced to document-based evidence.
The aim of this research is to develop an analytical optimisation methodology for the calibration of EnergyPlus models to detailed building and environmental data. Numerical multi-variable optimisation techniques will be used to analyse the calibrated model and optimise building control strategies for enhanced energy efficiency and occupant comfort.
ADDITIONAL SENSORS Temperature Probes People Counter Differential Pressure Sensor Soil Moisture Content
GENETIC ALGORITHM SEARCH
GUIDED REFINED SEARCH
ANALYTICAL OPTIMISATION
IDENTIFY SUB-SET OF "MOST PLAUSIBLE" PARAMETERVECTOR SOLUTIONS
UNCERTAINTY ANALYSIS
Structure of Calibration Methodology Evaluated in this Research [2]
Contact
Acknowledgements
Daniel Coakley, PhD Student Dept. of Mechanical and Biomedical Engineering, National University of Ireland, Galway
The author would like to acknowledge the support and advice of Dr. Padraig Molloy as well as Dr. Marcus Keane and Paul Raftery for their support in relation to building energy modelling.
Email:
[email protected]
References [1] Data from OECD (Organisation for Economic Cooperation and Development) and UNEP (United Nations Environment Programme) [2] Reddy et al., “Procedures for Reconciling ComputerFigure 3: Nursing Library, National University of Ireland, Galway. Calculated Results With Measured Energy Use Data” Ashrae Research Project 1051-RP, (2006)
I would also like to extend my gratitude to NUI, Galway for their financial assistance under the NUIG College Fellowship, as well as the continuous support offered by the Dept. of Mechanical and Biomedical Engineering and the Research Office.
