In This Issue

Jump to Page

1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121

THE ENERGY FUTURE OF EXISTING BUILDINGS IN BRUSSELS: BETWEEN PRESERVATION AND PERFORMANCE

difference between the measurement and the calculation, which means that my simulation is not necessarily close to the reality. There are multiple explanations for this: certain physical phenomena are not modelled by the calculation engine, such as hygroscopic inertia, for example. Our behaviour scenarios are less complex than actual family life. Nevertheless, we find that the dotted curves on either side of the bold curves form a significant range that represents the uncertainties about the model's input data. This illustrates well the importance of paying attention to these uncertainties when running simulations. In fact, it is necessary to be aware of the differences and the range of responses that can be obtained.

Fig. 4

Annual monitoring of consumption of the house in Noisiel (source Cerema).

Fig. 5

House in Noisiel. Step 2, static. Propagation of basic uncertainties (source: Cerema).

Fig. 6

House in Noisiel. Step 3, week by week. Annual curves on the confidence interval in kWh/week/m2. The measurement, in blue, also includes an uncertainty, as our sensors are not 100% reliable. Furthermore, the breakdown between hot water and heating is not clear (source: Cerema).



59