New Ways of Working: Linking Energy Consumption to People

REHVA Journal – December 2011

Ken Dooley

OlofGranlundOy

Sustainability Group Manager

Finland

ken.dooley@granlund.fi

Summary

Studies have estimated that residential energy consumption can be reduced by up to 30% if minor changes are made to our daily lives. In our study we aim to support positive behaviour change by enabling energy users to benchmark their consumption level and to compare energy consumption with their peers. We shall propose that consumption should be related to people and not exclusively to area and that by providing a new metric we shall empower people with the ability to claim and prove that they are living a low energy or low carbon lifestyle. We shall highlight the importance of understanding the dynamics of occupation when measuring the consumption of a building to ensure that people are not penalised for occupying their space efficiently, as is the case when only energy per unit area is considered. This paper proposes the new metric, Wh/m²h, in which the conventional annual energy consumption metric, kWh/m², is divided by total person hours. Total person hours is the total number of hours that all people have spent in the building during the year in question. The close relationship between measuring occupancy and controlling energy consuming systems is also examined. The benefits of this new metric are demonstrated by displaying the energy consumption results of a simulated case study building, which have been calculated for alternative population densities and alternative working hours per day. We also show that if occupancy is measured in order to understand our energy consumption then we can also use this knowledge to automatically turn on or off the energy consuming systems that are directly related to people. Finally, additional items to be developed in order to further support behaviour change are highlighted.

Keywords:         Wh/m²h, occupancy measurement, peer comparison, low energy lifestyle, behaviour change

Introduction

Supporting behaviour change

The European Union has legally committed to reduce greenhouse gas emissions by 20% by the year 2020 and Finland has targeted a reduction of at least 80% by 2050, these figures are both with reference to the 1990 level of emissions [1]. So how do we motivate the public to play their part in these reductions? The traditional method has been to offer financial incentives to encourage actions that reduce energy consumption such as installing energy efficient or renewable equipment. However, these incentives fail to establish a long term demand or interest in energy efficiency, as once the scheme comes to an end there is no longer any motivation to improve energy using components. Also, these schemes only reward discrete actions and fail to encourage small daily improvements in how we consume energy. Another drawback is that these programs do not appeal to all sections of society evenly, as the more affluent may be less motivated.

Behavioural scientists in the United States have estimated that domestic energy consumption can be reduced by up to 30% if minor changes are made to our daily lives. [2,3] These changes relate to household energy consumption and personal transportation. They are not expected to affect our standard of living and they include actions such as using low energy light bulbs, washing clothing at a lower temperature and having the correct tyre pressure in our cars. Our study focuses on supporting behaviour change by enabling energy users to benchmark their consumption level and to compare energy consumption with their peers. If the public are encouraged to reduce energy consumption but they cannot compare behaviour with their peers then how else can people evaluate success? Also if we are to create a society that values low energy living or low carbon living then this will become a precious social commodity. It will facilitate positive change to empower people with the ability to claim and prove that they are living a low energy lifestyle.

Current reward schemes

At present people receive prizes for living low energy lifestyles through reward schemes such as Earth Aid and My Emissions Exchange. When home owners first join the scheme, Earth Aid (www.earthaid.net) requests them to publically declare their energy and water bills for 1 year. This first year’s energy consumption, derived from the bills, becomes the baseline consumption level that all future years are compared to. If the building users reduce consumption relative to this first year then they receive rewards. These rewards are generally vouchers offering free consumer products and the value of these rewards increases relative to how much consumption has been reduced. The scheme is a powerful way to reward members of the public who have actively sought to reduce their consumption by actions such as turning unnecessary energy consuming items off, installing higher efficiency equipment or by installing renewable energy generation systems. My Emissions Exchange (www.myemissionsexchange.com) works in a similar way. A carbon footprint is calculated for each participant based on their first year’s energy billing and rewards are assigned by turning energy savings into carbon credits. These carbon credits are then sold to a third party on behalf of the homeowner.

If these schemes have a flaw, it is that rewards are allocated relative to each person’s own energy consumption history. If person A was once a high energy consumer and person B has consistently lived energy efficiently, then it is possible that person A will receive more rewards than person B even if person A consumes dramatically more energy per household. In other words those with a low consumption level are at a disadvantage as they can only improve their lifestyle by a smaller margin. In order to provide an understanding of how appropriate a household’s consumption level is, we require a format that justly compares energy consumption with that household’s neighbours regardless of the type of home, number of occupants or history of consumption.

Residential Buildings

Relating consumption to people

Through this study we aim to create a metric that facilitates domestic energy users to evaluate their consumption. We aim to empower and motivate people by enabling the following questions to be answered: am I living a low energy lifestyle? How does my consumption compare relative to my peers?

When we consider the current methods of evaluating and rewarding residential energy consumption, such as energy performance certificates or rebates for reducing energy consumption, these methods focus on the amount of energy consumed relative to the area of the household. They do not relate energy consumption to the amount of people who are responsible for the consumption. Energy metrics that relate energy to area, such as kWh/m², are extremely useful when considering the physical properties of a building that influence energy consumption such as wall and window u-values, building air tightness or the efficiencies of the heating and lighting systems. However, these metrics do not provide an understanding as to how effectively a household is being utilised once it is occupied, or in other words during the operations phase.

If we consider a simple scenario of a residential reward scheme in an apartment complex where all apartments are the same size and have two bedrooms. If the management company of the apartment complex were to offer a reward for the apartment that consumes the lowest amount of energy in a six month period who would win? Would it be an apartment occupied by a person living alone or an apartment that is occupied by a family of four people? If we evaluate energy consumption via the traditional method, by measuring it relative to area, then the apartment with only one occupant has a much greater chance of winning this competition due to the fact that one person can be expected to consume less energy than four people. Therefore, if we do not consider the dynamics of occupation of the spaces, the family of four are being punished for occupying their space more efficiently.

It stands to reason that the greater the number of people that occupy the fixed area of a residential household, the more efficient that household will be. This is due to the fact that many energy using systems consume a similar amount of energy regardless of how many people they serve. This concept of overlapping energy consumption applies to heating, lighting and appliances such as television. The energy consumption of other devices such as cooking appliance may increase marginally when serving more people but the energy consumption when cooking for two is not double that of when cooking for one person. There are also energy consumptions that relate directly to the number of people such as domestic hot water demand. This paper is not arguing that households should be occupied as densely as is practical but rather that residential energy consumptions could be evaluated in a fairer manner by considering occupancy. If we do not consider the number of building users when measuring residential energy consumption then we cannot compare our energy consumption with our friends, neighbours, or our fellow inhabitants of the city or country we live in. It is this social pressure that shall bring about the most effective change and shall engage the majority of citizens, not just those who are interested in sustainability or those who understand the opportunities for saving money.

The development of smart grid technologies (such as smart meters and smart systems) shall enable easier ways of sharing energy consumption data and controlling our energy consumption. Also, it is in the interest of environmental ministries around the world to promote the next generation of online social networking websites similar to Carbon Rally (www.carbonrally.com), Carbon Diet (www.carbondiet.org) or Step Green (www.stepgreen.org) which encourage the public to declare their carbon footprint and to compete with their friends to actively try to reduce it.

Measuring occupancy

Unlike commercial buildings, where time clocks are used to measure the movement of staff, occupancy is not readily measured in residential buildings. The closest example may be modern hotel key cards that provide power when the hotel key is in the correct location. These key cards are used to evaluate if the room is occupied but they do not measure how many people are occupying the room. It must also be noted that the data required to fully understanding the dynamics of residential occupancy can lead to fears of privacy invasion and security risks. For the purpose of this paper we shall assume that any data recorded shall be sufficiently encrypted as to dispel these fears.

Existing residential consumption comparison techniques compare household consumption with similar households. Similar households are generally considered as similar sized homes with the same number of people. The British Columbia Hydro power company enables their customers to compare their energy consumption through a program called Team Power Smart [4]. Customers can select type of building, home size (a range of areas is offered), home and water heating type and number of occupants. The scheme is optional and data is inputted by the customer to provide feedback as to how they are performing. Other companies provide their customers with bills that compare a household’s consumption with the average consumption of households that have the same number of people over the billing period.

Figure 1 Comparative billing example (Source: Opower.com)

If we are to empower people with the ability to claim and prove that they are living a low energy lifestyle we require a measurement that can be compared to all our peers and not just those who have similar sized families and homes. In the future this may be done through technologies similar to hotel key cards or by weighting our energy consumption by using an occupancy conversion factor. An occupancy conversion factor may be a simple method of relating energy to people. A list of indicative occupancy conversion factors may be seen in Table 1. The estimated annual energy consumption ratios shown in this table are not based on empirical data and have been chosen only to illustrate the benefit of such a factor.

Table 1 Annual energy consumption conversion factors for residential buildings relative to number of occupants (indicative only)

Non-residential Buildings

Relating consumption to people

Non-residential buildings also suffer when performance is measured relative to area. In this section we shall consider energy consumption; however the methodology shall also apply to other environmental impact factors such as carbon generation, water consumption and waste generation.

When analysing the energy use of non-residential buildings with a view to reducing building operating costs and reducing energy consumption the most common metric is kWh/m². As discussed earlier, this metric is extremely useful when considering the steady state properties of a building design but it does not provide an understanding as to how effectively a building is utilised during the operations phase. When considering how effectively and efficiently a building is being used we must consider (a) the number of hours per day the building is occupied and (b) how densely the space is populated.

If kWh/m² are used to assess an office building’s performance, then a building that has a shorter work day than is normal and provides a large area per person is perceived as being more energy efficient than a building that operates longer hours and has a more efficient floor plate. Therefore buildings that operate more efficiently are punished when we measure energy efficiency through a metric that does not relate energy to people. This shall be demonstrated in the case study described in section 3.5.

Occupied hours per day

When the number of hours a building is occupied per day is considered from the viewpoint of sustainability, it is highlighted that buildings contain significant amounts of embodied carbon emissions, they are associated with light and noise pollution and they are located on a plot that once was a greenfield site. It is clear that encouraging companies to combine departments with different working hours together to use the same space shall have a positive effect. Increasing the number of hours a building is used per day also has a positive effect from an energy efficiency point of view, as even when buildings are not occupied they consume energy in the form of systems such as: background heating, lighting (security, car park, corridor), exhaust ventilation (toilets, waste areas, staircases, technical spaces), reserve power systems such as UPS, servers and other communication equipment. It stands to reason once again that using buildings for as many hours as is possible shall have a positive effect and that we must begin to relate building performance to the number of hours per day, per month or per year it is occupied. Thus it is important to reward those who are using their buildings more efficiently. For example the total annual energy consumption of hospitals that operate for 24 hours a day should not be compared to similar hospitals that operate for 12 hours a day without some allowance being made for hours of operation.

Population density

Similarly to residential buildings, the energy consumption of commercial buildings consists of portions that are related to size of the building (heating, lighting), elements that are related to people (equipment energy such as computers, ventilation) and elements that are related to both of these (cooling). Buildings that have an efficient internal plan and hence have a low amount of floor area per person are also punished without considering people in the performance metric. If people are not considered then excessively large buildings shall continue to appear more energy efficient as energy consumption is divided by area. Therefore the larger the building, the lower the amount of kWh/m² shall be, as the energy related to the building population is spread over a wider area.

An alternative to kWh/m²

We require a metric that relates energy to density of population so that those who are utilising their buildings more efficiently are rewarded and not punished as is the current situation. This paper proposes the metric, Wh/m²h, where the conventional annual energy consumption metric, kWh/m², is divided by total person hours as shown in equation 1 below:

Energy per area per occupied hours:

                                                                                                                 (1)

where kWh₁ is the annual energy consumption, m² is the area of the building that is being measured, and where h₂, known as total person hours, is the total number of hours that all people have spent in the building during the year in question, h₂ is calculated as shown in equation 2 below:

                                                                                                                                    (2)

wheret is the number of hours that a person spends in the building during the year and n is the total number of people that visit the building during the year.

Case study: office building in Helsinki

By applying this metric to a conventional office building located in Helsinki, Finland, we can demonstrate the benefit of the new metric relative to kWh/m². Using dynamic thermal simulation software the annual energy consumption of an office building, whose geometry is shown in Figure 2 below, has been calculated for alternative population densities and alternative working hours per day. The results of the 9 comparison cases which were calculated may be seen in Table 2 below.

Table 2 Simulated energy consumption results of an office building showing kWh/m², kWh/person and Wh/m²h

If we consider cases 1, 4 and 7, which have similar working hours per day, the results show that when kWh/m² is used for the evaluation the building seems 3.1% more efficient when the building layout provides more area per person (12 m²/person compared to 8 m²/person). However, when kWh/person is considered the 12 m²/person configuration consumes 45% more energy than the when the population density is 8 m²/person. Wh/m²h provides a similar result as the 12 m²/person configuration consumes 45% more energy.

If cases 1 – 3 are considered, which have a similar population density, the results indicate that when kWh/m² is used for the evaluation the building seems 28% more efficient when the working day is shorter (6 hours compared to 12 hours). If Wh/m²h is used then the opposite is true and the working day of 12 hours is calculated as using 30% less energy when compared to a working day of 6 hours. Thus, the area only related metric is rewarding a shorter working day even though a longer working day is considered more efficient.

Figure 2. The geometry of the building used in the thermal simulation.

In order to promote a reduction in greenhouse gas emissions an alternative energy efficiency metric to kWh/m² is required that does not penalise buildings for incorporating a dense floor layout or a longer working day. It is important to note that the simulated energy results are based on the same building and only the population densities and hours of occupation are altered.

Controls

There is a close relationship between measuring occupancy and controlling energy consuming systems. If we measure occupancy in order to understand our energy consumption then we can also use this knowledge to automatically turn on or off the energy consuming systems that are directly related to people, similar to the hotel key card mentioned earlier.

For example if a company requires employees to register their arrival and departure from an office building via an electronic time clock system then this data can be used to calculate occupied hours per year and also to control energy consuming equipment. If an employee arrives at the office at 09.00 and leaves at 17.00 on a particular day then the occupancy data software can record 8 occupied hours for that person on that day. In addition the occupancy data can also be used to turn off equipment dedicated to that person before 09.00 and after 17.00. When that employee is not on the premises then the lighting over that person’s desk and the power to their computer can be automatically turned off. If that person had a single person office room then the ventilation and heating could be turned down to a minimum level or if that person was the last person to leave their particular floor or wing of the building then all systems could be automatically set to the night time mode. Understanding occupancy data such as when and where a person is on the premises does not only provide transparent energy consumption data but this data can be actively used to reduce energy consumption.

Figure 3 RTLS employee security card example. (Source: 9solutions.com)

Discussion

Technology such as real time location systems (RTLS) can also be used to measure occupied hours while providing the additional benefit of presence detection and increased levels of security. Electronic tags can be added to employee security access cards and a dedicated wireless network within the building can track the real- time location of each employee. This enables energy consuming items dedicated to each employee to be turned off based on location. For example if an employee is more than 10 meters away from their desk for more than 15 minutes then their desk could go to sleep mode by turning off lighting and other energy consuming systems associated with that person.

In this paper we have highlighted the potential flaws of relating energy consumption exclusively to area and we have proposed a new metric, Wh/m²h, that does not penalise buildings for incorporating a dense floor layout or a longer working day. However, there are many areas of the subject matter discussed in this paper that must be developed further such as: (a) deciding upon a method of measuring residential occupancy, (b) encouraging the public to openly declare their carbon foot- print and to compete with their friends to actively try to reduce it and (c) to develop integrated occupancy measurement and control technologies for commercial buildings.

References

[1]  Finnish Ministry of the Environment, “Climate change mitigation in Finland”, 2011, http://www.ymparisto.fi/default.asp?node=6039&lan=en (accessed 15.04.2011)

[2]  Laitner J.A., Ehrhardt- Martinez K., McKinney V., “Examining the scale of the Behaviour Energy Efficiency Continuum”, Proceedings of the ECEEE 2009 Summer Study: Act! Innovate! Deliver! Reducing Energy Demand Sustainability, 2009, pp. 217-223.

[3]  Gardner G.T., Stern P.C., “The Short List: The Most Effective Actions U.S. Households Can Take to Curb Climate Change”, Environment magazine, 2009, http://www.environmentmagazine.org/Archives/Back%20Issues/September- October%202008/gardner-stern-full.html, (accessed 15.04.2011).

[4]  KLOVANCE R., “Exposed! by our new Compare Your Home tool”, 2009, http://www.bchydro.com/news/unplug_this_blog/2009/exposed by_new.html, (accessed 15.04.2011).