Simulations with EnergyPlus

The simulations form part of the procedure of conducting a parametric analysis to optimize bio-climatic design variables on a hypothetical base-case building. This is fundamental to optimizing the thermo-physical and operational strategies behind a proposed PCM model as

3-Methodology

108

discussed in section 3.3. Chapters 6 and 7 discuss bio-climatic and PCM optimization respectively using computer simulations respectively.

The inclusion of the whole building energy analysis software, EnergyPlus allows all energy flows to be examined in a dynamic and integrated manner. Thus one limitation of the parametric technique is offset by the whole building energy calculation in the process. The limitation is the inability of the technique to capture the effect of changing an independent variable on more than one dependent variable.

Some advanced EnergyPlus simulation capabilities are limited in the DesignBuilder software (Ibarra and Reinhart, 2009) creating the need to export DesignBuilder models files into more powerful simulation engines. For instance it is currently not possible to model or simulate PCMs in DesignBuilder; but possible in the external version of EnergyPlus.

EnergyPlus is a ‘Qualified Computer Software’ for calculating energy savings for purposes of the energy-efficient commercial building in the USA (Pedersen, 2007). Expert users can get access to the source code allowing for third-party validation which adds to the

software’s credibility and long term reliability. EnergyPlus has been validated under the comparative Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs BESTEST/ASHRAE STD 140. It accurately predicts space temperatures which is crucial to energy efficient system engineering, occupant comfort, occupant health, system size, plant size (Ibarra and Reinhart, 2009).

EnergyPlus is an integrated simulation. This means that all three of the major parts, building, system, and plant, are solved simultaneously. The program is a collection of many program modules that work together to calculate the energy required for heating and cooling a building using a variety of systems and energy sources. The core of the simulation is a model of the building that is based on fundamental heat balance principles

3-Methodology

109 (EnergyPlus, 2011). See schematic in Figure 3-13

Figure 3-13 EnergyPlus energy calculation schematic (EnergyPlus 2010)

In this context, the energy flows investigated are the effects on electricity consumption and thermal comfort of:

 Bio-climatic design principles

 Incorporating PCMs into the building fabric

Originally, EnergyPlus simulate PCM performance using an enthalpy formulation called the Conduction transfer function (CTF) (Clarke, 2001). The algorithm models temperature dependent thermal conductivity, so simulations can be done with the PCM in any location within the surface structure. Because of the short time steps used in the finite difference solution algorithm, the zone time step can be reduced to correspond with the one minute minimum time step, small enough to produce adequate predictions.

More recently, EnergyPlus added a new solution algorithm that utilizes an implicit finite difference procedure called conduction finite difference (CFD). Computer models are mainly based on response functions or numerical finite differencing. Response function is good for solving linear equations that are time invariant while numerical finite differencing is good for simultaneous solution of complex non-linear problems while taking into consideration variant time steps. This level of accuracy for finite differencing aids spatial and temporal integrity of real energy systems (Clarke, 2001).

EnergyPlus software is most suitable tool for the simulation of PCMs incorporated in the building fabric (Tetlow et al., 2011). EnergyPlus has been demonstrated to simulate PCM

3-Methodology

110

performance in buildings by Pedersen (2007); and validated for simulating PCM systems in buildings by Chan (2011).

According to Castell, et al. (2009 ) who compare the capabilities of a commercial software EnergyPlus to calculate energy performance of PCM incorporated cubicle; the simulations in EnergyPlus do not reflect the effect of the PCM in the thermal behaviour of the cubicles.

This difference is especially important for the simulations with controlled temperature, where the improvements in the energy consumption are not observed in the simulations.

Some additional work must be done to consider the experimental weather data and to modify other parameters (such as infiltrations) to better represent the real behaviour of the cubicles and match better with the experimental results.

Chan (2011) uses statistical indices namely mean bias error (MBE) and root-mean-square error (RMSE) for error analysis to validate his simulated results from EnergyPlus and experimental results from Kuznik and Virgone (2009a). Kuznik and Virgone (2009a) conducted an experiment to examine the thermal performances of a PCM copolymer composite wallboard using a full scale test room. The presented the variables and results in detail to provide an opportunity for comparative validation for other researchers. The results from Chan (2011) show that all the cases slightly underestimate the surface temperatures with MBE ranging from −0.68% (northern wall on summer day) to −1.07%

(western wall on winter day); and RMSE values range from 2.07% to 3.9%. On the whole, it shows that the computer simulated results are in good agreement with the experimental data presented by Kuznik and Virgone (2009a).

Neto and Fiorelli (2008) compare a simple model using EnergyPlus and artificial neural network to simulate PCM performance. EnergyPlus consumption predictions presented an error range of ±13% for 80% of the tested database. Even though the error for EnergyPlus is higher than that of ANN, the algorithm used an implicit finite difference scheme coupled with an enthalpy-temperature function accounts for phase change energy accurately.

3.10 Limitations

Several limitations affected the research problems. They classified into:

 Filedwork stage limitations

 Simulation stage limitations

3-Methodology

111 3.10.1 Fieldwork stage

The limitations experienced when conducting the fieldwork include:

1. Access to buildings and building documents 2. Unreliable feedback from questionnaires 3. Inadequate records

4. Confidentiality for some companies 5. Insecurity

6. Sampling issues

7. Electricity consumption data

Access to the selected buildings was gained through different category of gatekeepers.

Gatekeepers could be an associate who works in the building, or through an introduction to one. The gatekeeper could also be the head of the organization itself. The latter proved to be the best type of gatekeeper because approval is sought from the head before the audit could commence in the first place. Plans to audit some suitable buildings failed due to a lack of approval from the head.

However, unreliable feedback was recorded from some of the buildings where the liaison officer in charge of assisting with the data collection was reluctant to help.

Another source of unreliable feedback was due to poor record keeping due to lacking management practices or negligence or lack of knowledge by the person in charge.

Some of the buildings housed sensitive offices or businesses facing confidentiality and even legal difficulties and so refused to participate in the audit exercise. However, a few agreed with the provision that they remain anonymous within the thesis.

Political insecurity was an issue that prevented the audit of buildings in a whole design climate; that covering Port-Harcourt city. An attempt to audit the building by proxy failed due to the complex nature of the data required.

There were limited resources to cover more data than the author collected in the 3 months it took for the fieldwork. This means that the period for data collection has to be rationalized.

Sources of error during the data collection stage include:

1. Poor records due:

o To poor management

o To ability of the person in charge

3-Methodology

112

o To erroneous data recorded in the questionnaire due to fraudulent activity in the company. For instance, to cover up for fraudulent purchase of diesel to power the back-up generators, the respondent may doctor the amount of diesel consumed

Gatekeeper access related issues directed the sampling style adopted in this study. Being a quantitative study, ideally sampling should be a random process that became impossible due to a lack of database-type information on suitable buildings, but also access into identified buildings.

Gatekeeper issues are attributed but not limited to privacy, security, market competition, and bureaucracy and administrative culture. As mentioned earlier, a whole region was rendered insecure due to political activity. Another issue was that the researcher could not access a list of mix-mode buildings in these cities to enable a random selected of these buildings to make it indeed a true experiment. For the buildings identified to be mix-mode, access to the type of data required was considered sensitive to some organisations and thus required approval from key management personnel that were inaccessible or simply declined to participate. In other instances, the key personnel may have given access but for bureaucratic and administrative culture, the staff member tasked with filling the

questionnaire will simply fail to do so. This may be due to incompetence, poor record-keeping, and power dynamics or simply to protect against the exposure of illegal activity within the organisation.

In the event that the energy audit was successful, some of the documents filled and

submitted proved to be erroneous or had missing data in the computing stage. This problem in some cases proved so poor that the cases had to be discarded.