The paper addresses the problem of insufficient knowledge on the impact of noise on the auto-regressive integrated moving average (ARIMA) model identification. The work offers a simulation-based solution to the analysis of the tolerance to noise of ARIMA models in electrical load forecasting. In the study, an idealized ARIMA model obtained from real load data of the Polish power system was disturbed by noise of different levels. The model was then re-identified, its parameters were estimated, and new forecasts were calculated. The experiment allowed us to evaluate the robustness of ARIMA models to noise in their ability to predict electrical load time series. It could be concluded that the reaction of the ARIMA model to random disturbances of the modeled time series was relatively weak. The limiting noise level at which the forecasting ability of the model collapsed was determined. The results highlight the key role of the data preprocessing stage in data mining and learning. They contribute to more accurate decision making in an uncertain environment, help to shape energy policy, and have implications for the sustainability and reliability of power systems.

The increasing demand for clean energy and the global shift towards renewable sources necessitate reliable solar radiation forecasting for the effective integration of solar energy into the energy system. Reliable solar radiation forecasting has become crucial for the design, planning, and operational management of energy systems, especially in the context of ambitious greenhouse gas emission goals. This paper presents a study on the application of auto-regressive integrated moving average (ARIMA) models for the seasonal forecasting of solar radiation in different climatic conditions. The performance and prediction capacity of ARIMA models are evaluated using data from Jordan and Poland. The essence of ARIMA modeling and analysis of the use of ARIMA models both as a reference model for evaluating other approaches and as a basic forecasting model for forecasting renewable energy generation are presented. The current state of renewable energy source utilization in selected countries and the adopted transition strategies to a more sustainable energy system are investigated. ARIMA models of two time series (for monthly and hourly data) are built for two locations and a forecast is developed. The research findings demonstrate that ARIMA models are suitable for solar radiation forecasting and can contribute to the stable long-term integration of solar energy into countries’ systems. However, it is crucial to develop location-specific models due to the variability of solar radiation characteristics. This study provides insights into the use of ARIMA models for solar radiation forecasting and highlights their potential for supporting the planning and operation of energy systems.

Public higher education sector is under a growing pressure worldwide to increase efficiency and improve the quality of its activities. Limited financial resources as well as detailed regulations and supervision of their spending are the important features of the public higher education sector. Another important and debated issue is the division of public money among higher education institutions (HEI). It is therefore crucial to create stimuli for the rational management of public funds by HEI and for the quality improvement of HEI services. One of the proposed ways to achieve the desired result is the comparative efficiency assessment of HEI activities. Setting clear reference points for HEI, such assessment may be treated as a substitute for market competition. This paper describes a comparative efficiency study of 19 Polish universities of technology. Detailed analysis of potential input, output and environmental variables describing the HEI efficiency model was carried out. The study used the CCR-CRS output-oriented DEA model. It was assumed that HEI had more influence on achieved results than on the amount of their resources. The economies of scale were also studied in relation to the efficiency achieved. Sensitivity of the model to data errors was tested.

The debate on the presence of economic benefits in the European Union (EU) is not over. The study responds unequivocally to this question, with the intensity of economic development in the countries that joined the European Union in 2004 and beyond twice as high as that of the countries that joined it this year, i.e. the EU’s old ones compared to the new ones; smoothness – 1.1 times and dynamics – 1.6 times. Another important trend for further development is that, as the level of economic development increases, its smoothness is diminishing. In respect of the context of the EP of all EU Members, it turned out that the higher intensity of enlargement was characterised by higher economic levels, with similar homogeneity and almost identical values for the dynamic indicator. The introduction to the article presents the context of the studies, i.e. two groups of EU Community countries are formed according to their level of economic development and the year of their accession to the Community, as well as a survey scheme. The literature review reveals the methods used to analyse the convergence of economic development in these countries, as members of the Community. The research methodology introduces the indicator of economic development of countries and provides a methodology for assessing its dynamics. The empirical part assesses the dynamics of economic development of both groups of countries and identifies trends in terms of convergence. The discussion section summarises the consolidation and destabilising factors in the EU and the importance of the study carried out in this context. The conclusions present the main results of the studies and outline their further directions. The results of the study can be used both in the EU and for the purpose-oriented decisions of its members on further economic development.