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.

The aim of this article is to enhance the understanding of how design companies perceive the benefits of Building Information Modeling (BIM) technologies application. BIM is recognized in the literature as a (potentially) powerful driver leading the construction sector towards sustainability. However, for design companies, the choice to invest in BIM technologies is basically an economic one. Specifically, a design company assesses economic benefits and efficiency improvements thanks to the application of BIM technologies. The article discusses the return on investments (ROI) in BIM technologies and reviews ROI calculation methodologies proposed by other authors. In order to evaluate BIM return on investment correctly practical ROI calculations are carried out. Appropriate methods, together with the relevant variables for ROI calculation, are developed. The study allows for adjusting the calculation method making it more accurate and understandable using the Autodesk Revit based ROI calculation of the first year.

Entrepreneurial intentions determine to a large extent the entrepreneurial behavior; therefore, the study of those intentions and the factors that influence them constitute a valid research area. The purpose of this regional comparative study was to design a new causal model of the formation of the entrepreneurial intention among young adults in Spain and Poland. Using the Structural Equation Modeling (PLS) methodology, the results show that subjective variables (beliefs, social norms, values) initiate the chain of effects that influence the action variables (motivation, self-efficacy, intention). Attitude is the nexus variable between both groups of variables. It is verified that there are no significant differences in the responses to the items or in the causal relationships of the model between both countries. This confirms the relevance of a homogenizing generational approach at a global level that allows the application of policies to promote the entrepreneurial intention for the entire segment. The proposed model takes into account and complements the previous designs, and is practical because it can be used at different levels of the education sector and by institutions promoting entrepreneurship and sustainability.

Effective solar forecasting has become a critical topic in the scholarly literature in recent years due to the rapid growth of photovoltaic energy production worldwide and the inherent variability of this source of energy. The need to optimise energy systems, ensure power continuity, and balance energy supply and demand is driving the continuous development of forecasting methods and approaches based on meteorological data or photovoltaic plant characteristics. This article presents the results of a meta-review of the solar forecasting literature, including the current state of knowledge and methodological discussion. It presents a comprehensive set of forecasting methods, evaluates current classifications, and proposes a new synthetic typology. The article emphasises the increasing role of artificial intelligence (AI) and machine learning (ML) techniques in improving forecast accuracy, alongside traditional statistical and physical models. It explores the challenges of hybrid and ensemble models, which combine multiple forecasting approaches to enhance performance. The paper addresses emerging trends in solar forecasting research, such as the integration of big data and advanced computational tools. Additionally, from a methodological perspective, the article outlines a rigorous approach to the meta-review research procedure, addresses the scientific challenges associated with conducting bibliometric research, and highlights best practices and principles. The article’s relevance consists of providing up-to-date knowledge on solar forecasting, along with insights on emerging trends, future research directions, and anticipating implications for theory and practice.

Construction of modern and durable asphalt and cement pavements requires high quality materials and suitable technologies that take into account sustainability concerns which are related to the environmental protection, mitigation and compensation for road construction effects on surface water and groundwater, soil, air, wildlife, landscape, vibration and noise. The objectives of this paper are to identify possible development directions of materials and technologies in road construction in the time perspective of approximately 30 years. In order to achieve that goal a nationwide Delphi survey with 150 invited experts was deployed. The study concluded that binding materials with improved viscoelastic range – and often with specific modifications – would continue to play a leading role. Furthermore, technologies that enable monitoring the state of road pavement condition in a continuous manner would be used to a greater range. Introduction of sensors into the pavement network would lead to the construction of “smart” roads while spreading of nanomaterial technology would improve the durability and reliability of road pavement construction.