Sustainable development and the increasing demand for equitable energy use as well as the reduction of waste of energy are the author’s social and scientific motivations. This new paradigm is the selection of a pertinent methodology to evaluate the efficiency of habitat thermomodernization, which is one of the scientific tasks of the presented study. In order to meet the social and scientific requirements, 380 buildings from the end of the last century (made of large plate technology), which were thermally improved at the beginning of the XXI century, were designed for a comparative analysis of the predictive modelling of heating energy consumption. A specific set of important variables characterizing the examined buildings has been identified. Groups of variables were used to estimate the energy consumption in such a way as to achieve a compromise between the difficulty of obtaining them and the quality of forecast. To predict energy consumption, the six most appropriate neural methods were used: artificial neural networks (ANN), general regression trees (CART), exhaustive regression trees (CHAID), support regression trees (SRT), support vectors (SV), and method multivariant adaptive regression splines (MARS). The quality assessment of the developed models used the mean absolute percentage error (MAPE) also known as mean absolute percentage deviation (MAPD), as well as mean bias error (MBE), coefficient of variance of the root mean square error (CV RMSE) and coefficient of determination (R²), which are accepted as statistical calibration standards by (American Society of Heating, Refrigerating and Air-Conditioning Engineers) ASHRAE. On this basis, the most effective method has been chosen, which gives the best results and therefore allows to forecast with great precision the energy consumption (after thermal improvement) for this type of residential building.

One of the basic objectives of the European Union’s energy policy is to obtain and use energy in a sustainable way. Multiple-criteria decision making (MCDM) methods, in particular linear ordering based on the synthetic variable procedure, are used for comparative analyses of the level of energy sustainability. Despite many studies, the problem of choosing the optimal ordering method is still not fully resolved. This paper presents an original procedure that facilitates the selection of an effective method of the linear ordering of multi-feature objects for the evaluation of sustainable energy development of regions. What is understood as the effective ordering of regions is not only the effective ranking of objects but also their effective clustering. In order to obtain the best results of linear ordering, the authors put forward a multi-stage optimization of the selection of the method of ordering and normalization of diagnostic variables. Analysis of variance was used for the assessment of the object ranking quality, while for the assessment of the object clustering quality, an innovative approach was presented based on the analysis of the empirical distribution of the frequency of occurrence of the distance between objects. The linear ordering method, selected on the basis of the procedure proposed in the paper, was used to assess the energy sustainability of Polish regions. The calculations and analyses were carried out using the set of indicators developed by the authors. They characterize the sustainable energy development of regions in the social, economic, and environmental dimensions.

Energy efficiency in the building industry is related to the amount of energy that can be saved through thermal improvement. Therefore, it is important to determine the energy saving potential of the buildings to be thermally upgraded in order to check whether the set targets for the amount of energy saved will be reached after the implementation of corrective measures. In real residential buildings, when starting to make energy calculations, one can often encounter the problem of incomplete architectural documentation and inaccurate data characterizing the object in terms of thermal (thermal resistance of partitions) and usable (number of inhabitants). Therefore, there is a need to search for methods that will be suitable for quick technical analysis of measures taken to improve energy efficiency in existing buildings. The aim of this work was to test the usefulness of the type Takagi-Sugeno fuzzy models of inference model for predicting the energy efficiency of actual residential buildings that have undergone thermal improvement. For the group of 109 buildings a specific set of important variables characterizing the examined objects was identified. The quality of the prediction models developed for various combinations of input variables has been evaluated using, among other things, statistical calibration standards developed by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). The obtained results were compared with other prediction models (based on the same input data sets) using artificial neural networks and rough sets theory.

Due to the large quantities of municipal waste generated, their harmful effects on the environment should be minimized. The rationalization of waste management is therefore necessary to achieve a more sustainable development system. In order to support the decision-making process for municipal waste management, this document focuses on developing models for practical use by local authorities in forecasting and managing the size of waste stream in their area. This action, because of its specificity, is a difficult task, especially because of the systemic changes made and the territorial differentiation and changes in the living level of the population. The work presents studies conducted in 2479 municipalities for which mass accumulation index forecasts were developed, using selected methods based on readily available input variables that have not yet been used (structure municipalities and typology of municipalities by scope of influence). The studies confirmed the hypothesis that the amount of municipal waste collected from households depends both on the administrative type of the municipality and on the factors related to the location and socioeconomic function of the area. The inclusion of localization and socioeconomic factors, which so far were not used to model the municipal waste stream, allowed for the reduction of the prediction error of this indicator. Relevant waste stream forecasts will allow local governments to achieve more effectively the objective of sustainable waste management and thus reduce their environmental impact. The achievement of this objective will be possible not only through the preparation of infrastructure to serve the projected waste volumes; it will also identify the waste management areas where the municipal waste reception process is inadequate. Thus, it will help to eliminate illegal processing and the landfill of waste.

The method based on rough set theory (RST) was used in the study to establish the rate of mass accumulation of waste in households in rural areas, which are characterised by different economic types, in case of which traditional statistical analyses are usually hardy reliable. The following indicators available in the General Statistical Office’s statistics were used in the analysis: population density, income level, main source of income, economic type of the municipality, area of agricultural land, age of the buildings and participation of gaseous fuels in meeting heat demands. The method shown should not be considered as a competition for statistical methods, but it could complement them, especially in cases when there are few objects to analyse, the more so as it proves useful in cases where input data are general, imprecise and uncertain. As has been shown in the study, with such data and a small number of objects, the relative error of estimation was 13% on average.