In parallel, there is growing trend towards outsourcing of the energy management of commercial buildings. This outsourcing usually requires one party to contractually deliver a near constant level of occupant comfort while the other party pays a fixed annual fee for this service. The arrangement is known as Performance Based Contracting or PBC. To date, PBC has been fraught with unacceptable risks of performance and costs uncertainty due to misalignment between predictions and measured energy consumption, particularly on the side of the supplier, because a deep understanding of the building and plant operations has been difficult to assess prior to entering such contracts.
Original Research Proposal
The research builds on the recently completed CIFE report on how design stage BEPS models can be improved in accuracy and the effort to produce them reduced. One of the major hurdles in this process of building these models is the accuracy of the building geometry. The Building Parser (BP) project has recently introduced a method to produce accurate internal building geometry from a laser or Matterport scan. This project has been designed to test how accurate that geometric data is in terms of energy simulation requirements and how it can be automatically imported to a BEPS system such as EnergyPlus (see Figure 2).
Four buildings will be identified for the research work:
• Building 1: existing conventional building with an as-built BIM
• Building 2: existing conventional building without an as-built BIM
• Building 3: in the commissioning stage built on the basis of a BIM
• Building 4: in the commissioning stage built without a BIM
The primary purpose of the research is to explore the relative geometric accuracy of the BEM generated by the Building Parser in terms of producing an input for BEPS, versus that of an existing BIM or as-built drawings, depending each time on the building being examined.
The research method has been divided into five separate sections:
1. Building the hypothesis – the Building Parser method is at least as accurate as the conventional manual methods of importing building geometry to EnergyPlus but significantly more efficient. The steps described below are summarized in Figure 3.
a. Based on conventional methods, build a fully calibrated BEPS model for building #1, using the available geometric data.
b. Repeat the process using geometric data from the Parser output module
c. Check and compare the accuracy of both geometry and BEPS outputs versus actual building and the effort required to complete the BEPS model
d. Refine the method of geometric data export from the Building Parser, if required
e. Repeat this procedure for Building #2 with as-built drawings
f. Build a Best Design BEPS model for building #3, using the available geometric data
g. Repeat the process using geometric data from the Parser output module
h. Check and compare the accuracy of both geometry and BEPS outputs versus actual building and the effort required to complete the BEPS model
i. Refine the method of geometric data export from the Building Parser, if required
j. Repeat this procedure for Building #4 with as-built drawings
Figure 3. Iterative process to develop and evaluate the proposed approach. These steps will be repeated per building.
2. Reporting and refinement
a. Refine the geometric data extract module based on the results found, if necessary
b. Determine the accuracy requirements/capabilities for the Building Parser and BEPS models. To this end, the term “accuracy” in cases of energy simulation and point-cloud generated needs to be formally defined.
c. Publish report and PR Papers
This project provides a significant test of the ability of the Building Parser System to deliver detailed and accurate geometry to a BEPS model and the effect of this approach to obtaining as-built building information on the BEPS model. It will also contribute to assess the accuracy of the developed scan-to-BEPS pipeline that requires minimum human intervention (limited only to the addition of thermal properties).
3. Non-geometrical parameters’ BEPS model
The previous study, which utilized data from two buildings in different geographical locations and of very different construction and geometry, resulted in a modelling methodology which provides quantitative guidelines for design stage BEPS models. The study taught us about the various parametric groups necessary to focus on leading to an accurate yet simple model. It is now proposed to onwards develop that methodology to include an accurate model using the simplest possible or minimal dataset from the building in question.
This can be achieved through the following:
a. Collection of utility data and preferably interval data for all energy sources (this can be recorded post project commencement, if necessary)
b. Geometry data collection by laser scan and/or BIM database
c. Examination of material thermal properties from construction specifications
d. Detailed analyses of HVAC systems as implemented versus Ideal loads
e. Best Design BEPS model development
f. Detailed analysis of the probability of using the Ideal Loads model scenario as an accurate predictor of optimized energy usage in both buildings
g. Examine the outcomes for model accuracy and possible improvements to shorten time and effort required
4. Assessment of the risks inherent in Performance Based Contracting
More efficient building energy performance contracting requires appropriate allocation of risk. Appropriate allocation can be achieved by allocating risk to the party involved in the building life-cycle. The possibility to quantify the influence of BEPS input parameters’ variability on building energy performance will support stakeholders in developing mechanisms to sort out performance responsibilities among designers, owners and building operators.
It opens up possibilities of defining contractual solutions to communicate about anticipated use patterns between owner, building operators, and design team in the design phase. This enable and support the introduction of contractual solutions to allocate risk factors to the stakeholders that can most effectively mitigate the risk. Performance Based Contracting (PBC) requires appropriate allocation of risk within designers, owners and building operators to pursue and ensure building performance delivery. Firstly, the contracting parties agree upon the definition of these risk factors and identify the variables related to each category based on their respective risk mitigation capabilities. Next, the performance contractor develops a BEPS model which allows the quantification of the impact of these risk factors on the building energy performance through a sensitivity analysis and risk assessment. Finally, the parties agree on a financing structure which determines how incentives / penalties are distributed in the cases where these risks are incurred during building operations.
Risk assessment consists of variating model inputs to see the effects on model outputs in order to determine the relation between independent and dependent variables. The quality of analysis’ result mainly depends on the quality of the models and its input data. BEPS inputs are affected by uncertainties that may have significant effects on outputs and are important to be considered. Having accurate BEPS input improve accuracy of predictions.