LA ADOLESCENCIA PROPIAMENTE DICHA 1 DESARROLLO COGNOSCITIVO

Wall - solid masonry 2.4 [136]

Wall - modern building standards 0.45-0.6 [136]

Wall - best methods 0.12 [136]

Single glazing 6 [156]

Double glazing 1.4-3.12 [156]

Roof 0.16-0.25 [156]

Figure 4.4: A selection of typical U-values

1. ^dQ_dtⁱ is the total power required to maintain the building temperature;

2. max(0,^dQ_dtⁱ) is the total power required to cool the building;

3. max(0,−^dQ_dtⁱ) is the total power required to heat the building.

H_i and H_e correspond to the ‘leakiness’ of the wall towards the interior and exterior of the building respectively. These are derived by multiplying the surface area (m²) by the thermal transmittance or U-value (W/m²/K). The surface area of the building was estimated manually (40,000 m²) but such information could also be obtained from the floor area in OpenRoomMap and an estimate of ceiling height and roof-pitch. U-values are normally quoted for a single surface and a typical value suggested by MacKay for best building methods of 0.15 was adopted [136]—the building in question won an architectural award for its heating and cooling efficiency.⁷ The model uses separate U-values for the inner shell (U_i) of the wall and the outer shell (U_e) and so it is further assumed that the outer shell has 2.5 times the thermal resistance of the inner shell. Given that U-values combine in the same manner as resistors in parallel:

1 U = 1

U_i + 1

U_e (4.3)

and substituting U_e = 2.5U_i gives:

U_i = 3.5U = 0.53 (4.4)

U_e = 3.5U

2.5 = 0.21 (4.5)

Although this model is very simple, it does produce acceptable results (see Section 4.2.3) and so serves the purpose of demonstrating that little input data is genuinely necessary;

of course, it could be replaced with more sophisticated physics in subsequent versions.

01 Nov

2009 01 Dec

2009 01 Jan

2010 01 Feb

2010 01 Mar

2010 01 Apr

2010 01 May

2010 01 Jun

2010 01 Jul

2010 01 Aug

2010 0

50 100 150 200 250 300 350

Powerconsumption(kW)

Metered consumption HVAC Lights PCs Machine rooms Other

Figure 4.5: Daily breakdown (Nov 09 to Aug 10) shows trends in electricity consumption are correctly estimated [177]

11 Jan

2010 13 Jan

2010 15 Jan

2010 17 Jan

2010 19 Jan

2010 21 Jan

2010 23 Jan

2010 25 Jan

2010 0

50 100 150 200 250 300 350

Powerconsumption(kW)

Metered consumption HVAC Lights PCs Machine rooms Other

Figure 4.6: Half-hourly breakdown (Jan 2010): electricity requirements during winter vary mostly due to lighting needs [177]

12 Jul

2010 14 Jul

2010 16 Jul

2010 18 Jul

2010 20 Jul

2010 22 Jul

2010 24 Jul

2010 26 Jul

2010 0

50 100 150 200 250 300 350

Powerconsumption(kW)

Metered consumption HVAC Lights PCs Machine rooms Other

Figure 4.7: Half-hourly breakdown (Jul 2010): cooling dominates the electricity require-ments during summer [177]

ments of electricity consumption as recorded by the electricity company. The categories in the breakdown are as follows:

HVAC is the output of the heat model for the building. Initially only cooling is consid-ered to account for electricity usage.

Lights includes lighting within offices (modulated according to the occupancy of the building) and in public areas (modulated according to a timer function).

PCs covers the energy use of personal computers and monitors in offices, assuming that the PC itself is left on continuously whereas the monitors are switched on or off according to the occupancy of the building. Both are assumed to consume 70 W.

Machine rooms considers servers, uninterruptible power supplies and air conditioning units in the machine rooms. This is a mixture of sub-metered readings and manual estimates.

Other contains minor items from the OpenRoomMap inventory such as printer idle power, telephones and a small number of electric heaters.

Notable from the graph is that the predicted consumption displays similar trends to the true measured value. Over the annual period the load on the HVAC system increases

01 Nov

2009 01 Dec

2009 01 Jan

2010 01 Feb

2010 01 Mar

2010 01 Apr

2010 01 May

2010 01 Jun

2010 01 Jul

2010 01 Aug

2010 0

50 100 150 200 250 300 350

Powerconsumption(kW)

Metered consumption Heating Cooling Lights PCs Machine rooms Other

Figure 4.8: The model underestimates combined heating and cooling energy consumption during winter. Note that metered consumption here includes both electricity (recorded half-hourly) and gas (recorded only monthly and interpolated) [177]

during the summer months and falls to nothing over the Christmas period when the building is quiet and the exterior temperature is low. Unfortunately, no ground truth data disaggregated by function could be obtained as the equipment in each category is distributed throughout the building so a very large number of sensors would be required.

Nevertheless, the HVAC estimate fits the trends in the metered electricity consumption particularly well in the summer months when the heating load is highest, suggesting that it is indeed responsible for many of these variations.

Figure 4.6 shows a two week period in January. The peaks in consumption during working weekdays are clear in the model and the breakdown shows that this is mostly due to lights being switched on (in offices). Figure 4.7 shows a two week period in July. In this case the HVAC energy usage is significantly higher due to higher outdoor temperatures.

The effect of including heating in the model is now considered. The heating system is assumed to be 70% efficient and from the gas consumption over the summer when no space-heating is needed an additional cost of 1.4 kW for water heating is derived which is included in the ‘Other’ category. There is what seems to be a more significant deviation from the measured trace (Figure 4.8, showing both electricity and gas⁸). However, this

8Converted to kWh in accordance withConversion factors - Energy and carbon conversions - 2010 update (CTL113) http://www.carbontrust.co.uk/publications/pages/publicationdetail.aspx?

id=CTL113

is due in part to the fact that the gas consumption data for the building is measured monthly and must therefore be interpolated linearly so day-scale changes in consumption as predicted by the HVAC model are not reflected in the measured consumption trace (Figure 4.9). There are many factors which could be altered to obtain a better fit, such as changing the U-value of the building, the efficiency of the heating system or the fixed point temperature but this is not done for fear of over-fitting what is a very simple model. The results are sufficient to show how a personal energy meter could provide individuals with useful insights into the breakdown of their consumption without the need for extensive device-level metering.