عدم اطمینان و مدل سازی مصرف انرژی : تجزیه و تحلیل حساسیت برای یک مدل انرژی داخلی در مقیاس شهر

This paper presents the development and evaluation of the Belgrade Domestic Energy Model (BEDEM) for predicting the energy consumption and carbon dioxide (CO2) emissions of the existing housing stock. The distribution of energy use in relation to the end use is estimated as: space heating, 71%; light and appliances, 15%; water heating, 9%; and cooking 5%, while the distribution of CO2 emissions is space heating, 59%; light and appliances, 22%; water heating, 13%; and cooking 6%. Local sensitivity analysis is carried out for dwellings of different type and year built, and the largest normalized sensitivity coefficients were calculated for parameters which almost exclusively influence space heating energy consumption in housing. For all input parameters under investigation, the effects of the input uncertainty were linear for a moderate range of input change (Δx = ±10%) and superposable for a small range of input change (Δx = ±1%). However, the non-linear and non-additive properties of some input parameters over the wider range hinder the development of a simple but reliable model for estimating energy and CO2 reductions. The findings show that the uncertainty in the stock models predictions can be large and more work is needed in the area of the predictive uncertainty of stock models.

In 2010 the Serbian government adopted a national energy savings target of at least 9% based on 2008 total energy consumption by the year 2019 [1]. This means that the country should ensure energy savings of about 8.8 TWh over the next seven years. Achieving this target will require urgent carbon reductions in most if not all sectors of the economy. For some sectors, such as transport, energy and carbon savings are seen as being difficult to achieve [2], whereas the building sector has been identified as having potential to deliver a larger contribution to energy savings [3] and [4]. Belgrade is both the political and economic capital of Serbia where live nearly quarter of all households in Serbia [5]. The Belgrade housing stock also accounts for nearly 40% of the city's total annual energy consumption and as such is considered to have a significant potential for application of various energy-efficient measures and renewable energy technologies [6] and [7]. However, a detailed knowledge and understanding of the nature of the energy use and CO2 emissions attributable to the housing stock is needed to strengthen the evidence base and to support the development of an effective energy efficiency and carbon reduction strategy. Energy consumption of the Belgrade housing stock is driven by a range of inter-related factors, including variations in the physical characteristics of buildings, the building services, and the behavior of occupants. There are over six hundred thousand dwellings that differ considerably in their size, shape, and construction. The vast majority of dwellings (∼90%) were constructed after World War II using a wide range of materials and techniques, from uninsulated solid brick walls to sandwich wall systems of precast concrete and clay blocks [1]. There is also a great variety in types and efficiencies of space heating system installed in dwellings and the types of fuels used. Occupant behavior, which to a large extent reflects household socio-demographic characteristics, greatly influence space and water heating consumption (other than for district space heating system), and the usage patterns of lights and electrical appliances.

The results presented herein suggest that mean indoor temperature, efficiency of space heating system, external air temperature, and window U-value with the highest sensitivities of Si,j = 1.5, Si,j = 0.60, Si,j = 0.38, and Si,j = 0.23, respectively, almost exclusively influence space heating energy consumption and, therefore are the most influential factors of dwelling energy use and CO2 emissions. Hence, if there is a large error/uncertainty associated with these parameters it is very likely that model predictions will be inaccurate. However, knowing only these inputs is not a sufficient condition to obtain an accurate result, as the results of sensitivity analysis show large differences in the influence of input parameters in relation to the dwelling built form and year built. Therefore, adding accurate values for the factors such as U-values of wall, roof and floor, air tightness, and building geometry considerable increases the probability of obtaining an accurate result. For example, all the parameters with high sensitivities (listed above) have 30–70% more influence on the CO2 emissions of SFH and older MSB than on the emissions from post-1981 MSB. This suggests that SFH and older MSB are the most sensitive to changes in their input parameters, while newer MSB are the least susceptible. Hence, it is very likely that measures targeted at SFH and older MSB will have a larger effect than in new dwellings. However, SFH are also more susceptible to the underperformance of almost all input parameters under study than any of the other building categories because of their greater exposed envelope area and higher heating demands.