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The paper presents the residential sector building typology, thermal energy balance, and scenarios prepared at several levels of sector segmentation to assist the design of low-carbon development policies for Albania, Serbia, and Montenegro. The research is breakthrough for developing Europe and could be replicated in its countries. The paper describes methodological steps and selected results. First, representative building types were identified; their energy performances by end-use, retrofit packages, as well as associated costs were assessed. Second, this information was inserted into a bottom-up simulation model prepared in the Long-range Energy Alternatives Planning System (LEAP) software. Using it, sector energy balances, the reference scenario, as well as moderate and advanced low-carbon high-thermal-comfort scenarios were prepared. The low-carbon scenarios assumed ambitious regulatory and financial policies. It was found that due to fuel poverty partial and intermittent heating is a typical situation; therefore, the thermal demand as predicted by the models applied to the different segments of the dwelling stock is much higher than its actual consumption. Also, actual consumption by energy source was found not fitting official energy balances because households use more wood and more heating systems than officially reported. In 2030, the moderate and ambitious scenarios lead to a reduction of CO2 emissions by 23-73% and 16-73% respectively versus the reference, offering however at the same time higher thermal comfort. The priority is to retrofit small buildings constructed after 1991 in Albania and those built in 1971-1990 in Montenegro and Serbia. Assuming the discount rate of 4% and counting saved energy costs as benefits, almost all scenarios are cost-effective as a whole on the country level, however not for many building categories. Therefore, not only saved energy costs but also other benefits should be monetized and compared to the scenario costs that present the next research opportunity. [ABSTRACT FROM AUTHOR]

[Display omitted] In the present research, a high-resolution, detailed electric load dataset was assessed, collected by smart meters from nearly a thousand households in Hungary, many of them single-family houses. The objective was to evaluate this database in detail to determine energy consumption profiles from time series of daily and annual electric load. After representativity check of dataset daily and annual energy consumption profiles were developed, applying three different clustering methods (k-means, fuzzy k-means, agglomerative hierarchical) and three different cluster validity indexes (elbow method, silhouette method, Dunn index) in MATLAB environment. The best clustering method for our examination proved to be the k-means clustering technique. Analyses were carried out to identify different consumer groups, as well as to clarify the impact of specific parameters such as meter type in the housing unit (e.g. peak, off-peak meter), day of the week (e.g. weekend, weekday), seasonality, geographical location, settlement type and housing type (single-family house, flat, age class of the building). Furthermore, four electric user profile types were proposed, which can be used for building energy demand simulation, summer heat load and winter heating demand calculation. [ABSTRACT FROM AUTHOR]