The challenges of the modern world require transformations in the energy market towards the possible reduction of consumption and greater use of renewable sources. The conducted research of consumers of this market confirms that the behaviour in the field of increased use of renewable energy is burdened with cognitive errors and motivational factors, which makes it difficult to conduct quantitative research. Electricity demand forecasting can be modelled using selected quantitative methods. In this way, not so much the behaviour, but the result of the consumer’s behaviour is predicted. The research presented in the article has been divided into two parts. The aim of the first one is to study the prospects of a greater share of renewable sources in obtaining energy in Poland, based on the attitudes and opinions of consumers on the retail energy market, legal regulations and the energy balance. The aim of the second part is to build forecasts of daily, weekly, monthly and quarterly electricity consumption in Poland, including the prediction of the RES share, using selected machine and deep learning methods. The analyses used the time series of daily electricity consumption in Poland from 2015–2021; the ENTSO-E data was obtained from the cire.pl website. Depending on the adopted forecast horizon, the forecasting method with the lowest MAPE error was exponential smoothing, SARIMA and NNETAR. An evolution of energy consumers’ attitudes towards pro-ecological and pro-social sensitivity and understanding of the importance of RES for the economy was also observed.

There is robust evidence that homelessness and the associated life conditions of a homeless person may cause and exacerbate a wide range of health problems, while healthcare for the homeless is simultaneously limited in accessibility, availability, and appropriateness. This article investigates legal frameworks of health care provision, existing knowledge on numbers of homeless to be considered, and current means of health care provision for four EU countries with different economic and public health background: Austria, Greece, Poland, and Romania. National experts investigated the respective regulations and practices in place with desk research. The results show differences in national frameworks of inclusion into health care provision and knowledge on the number of people experiencing homelessness, but high similarity when it comes to main actors of actual health care provision for homeless populations. In all included countries, despite their differences in economic investments and universality of access to public health systems, it is mainly NGOs providing health care to those experiencing homelessness. This phenomenon fits into conceptual frameworks developed around service provision for vulnerable population groups, wherein it has been described as “structural compensation,” meaning that NGOs compensate a structural inappropriateness that can be observed within public health systems.

One of the stages of the comparative analysis of multivariate objects is the data normalization. There are many procedures of the normalization of the variables described in the literature. The choice of the normalization method is one of the most crucial steps for the researchers as it has a profound effect on the results of the analysis. The main goal of the present study is to examine the sensitivity of the result of linear ordering of objects, using three selected normalization methods, in calculating a synthetic taxonomic measure TMAI to create ratings of 15 building materials companies, listed on the Warsaw Stock Exchange. The study was made for the years 2013 and 2014. The conducted study shows that the use of different normalization formulas of variables can cause the change of the results of the companies classification, which does not result neither from the data structure change nor the effectiveness modification of their operations.

The article raises the issue of forecasting prices of broiler chickens. The forecasts were generated on a set of the weekly time series of mentioned prices in the 2011-2017 period. The forecasting methods which were used in this research are adaptive methods: simple random walk model and creeping trend with fixed segments of linear trends equal 5, 7, 9 and 11 periods. The accuracy of forecasts was verified in retrospect by preparing forecast in the past, forecasting errors and graphical analysis. Both the crawling trend model and the random walk model with greater weight take into account observations closer to the forecasted values, which worked well in the case of fairly large distortions of random variations in a series of purchase prices for broiler chickens. Reducing the length of the segment in case of large random fluctuations and breakdowns of the trend allows to obtain smaller forecast errors.