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.
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 European Union (EU) energy policy for sustainable development has been the topic of continuous debate, research, and analysis, which frequently focused on objectives and the evaluation of ...quantitative and qualitative performance. Different approaches can be used for the assessment of sustainable development goals. The authors of the article conducted a literature review of relevant research papers dated 2016–2020. The most common are quantitative methods based on hard data. Some qualitative studies based on soft data are also available but rare. This article proposes hybrid Rough Set Data Envelopment Analysis (DEA) and Rough Set Network DEA models that integrate both approaches. Also, the models allow the inclusion of uncertainty in the underlying data. The article uses hard data of the International Energy Agency (IEA) and the results of the EU survey regarding the influence of the socio-economic environment on CO2 emissions in EU countries. The authors demonstrate that multifaceted and objective assessment is possible by merging concepts from the set theory and operational research.
Researchers and practitioners argue that in the global context of the Fourth Industrial Revolution, also labelled Industry 4.0, the regional dimension of industrial development remains equally ...essential. A region that effectively implements the concept of Industry 4.0 can accelerate by enhancing the manufacturing energy efficiency, thus contributing to the goals of the “Green Deal” policy. Therefore, to support the policy-making process, it is necessary to develop analytical tools exploring the determinants of the Industry 4.0 development. This paper presents a methodology of strategic analysis of a region in terms of the Industry 4.0 development potential. The core of the methodology is an extended Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. The study identifies regional strengths and weaknesses, external incentives and disincentives, internal opportunities and threats, and external opportunities and threats with regard to the development of Industry 4.0, related technologies and the potential of increasing manufacturing energy efficiency. The research procedure is exemplified by the case of Podlaskie Voivodeship in Poland. The results of this study demonstrate the robustness of the proposed approach. The elaborated methodology can be used by decision-makers in designing strategies for the development of fourth-generation industry at a regional level.
The complexity and speed of change in technological systems pose new challenges to technology management. Particular attention should be given to the issue of modelling the uncertainty of assessments ...and creating rules for determining the weights of the technology assessment criteria. The article aims to present a comprehensive hybrid technology prioritisation model based on the Data Envelopment Analysis and the concept of Rough Sets. The technology prioritisation process that uses the proposed model includes three consecutive stages: (i) the formulation of technology assessment matrix, (ii) the removal of the criteria redundancy based on indiscernibility relation defined in the Rough Set Theory, (iii) the development of rough variables and prioritisation using the DEA super-efficiency model. The combination of DEA and RS is a unique proposal to classify and rank objects based on the tabular representation of their conditional attributes under circumstances of uncertainty. Application of the developed hybrid model to the real data of the technology foresight project “NT FOR Podlaskie 2020” positively verified the assumed effects of its use. The obtained results allow a more objective and rational justification of the chosen technology, simplification of interpretation and better authentication of results from the perspective of decision-makers.
The article presents the concept of environmental efficiency analysis based on the method of Data Envelopment Analysis in the case of the existence of desirable and undesirable results. Theoretical ...considerations are illustrated by a case study of European countries and evaluation of productivity taking into account not only economic growth but also effects which are undesirable and impossible to eliminate entirely, such as the impact on the environment. The differences in the results are explained by the relationship between policies aiming at supporting research and development with the use of the Tobit regression model. The added value of this work is to propose an integration of environmental DEA method with the concept of technological competitors. The possibility of applying the concept of DEA to technological competition was presented in the form of classification and benchmarking of the European countries. It is concluded that European countries are highly diversified in regard to the efficiency of environmental performance.
This paper presents the assessment of the European Union member states in terms of the circular economy (CE) targets, using a combination of the Data Envelopment Analysis (DEA) method and factor ...analysis. This approach fills in the existing knowledge gap by providing an innovative methodology of an objectivised comparative evaluation of the degree of implementation of the CE principles by the EU countries. Assessing countries’ performance in achieving the goals of the circular economy is a challenge due to the lack of a generally accepted methodology, the multitude of indicators, and the insufficient data. Countries may be compared in a narrow way, according to single indicators, but a more holistic synthetic assessment of countries is also needed to determine their position against each other. In such cases, DEA may be successfully used. The study resulted in the identification of two clusters of countries with similar profiles of relative efficiency in the CE goals’ implementation. It was concluded that the position of a particular country in achieving the CE aims was strongly correlated its GDP per capita. Moreover, factor analysis showed that many CE indicators are strongly correlated with each other and may be aggregated into five meta-indicators (factors): Recycling rate of general waste, Waste production, Jobs and investments, Recycling rate of special waste, and Circular material use rate. In addition to simple rankings and indication of benchmarks, the article offers a novel concept of technology competitors which was used to group units competing for positions in the ranking.
The primary problems pertaining to productivity or - more precisely - efficiency are: how to define it and how to measure it. This article studies technical efficiency in Stochastic Frontier Analysis ...(SFA) - the input-oriented frontier model - in the construction industry and compares it with Data Envelopment Analysis (DEA) results. The models explored in this paper were constructed on the basis of two outputs and personnel cost as an input. The research sample consisted of European countries. The aim was to determine whether there are substantial differences in estimation of efficiency derived from those two alternative frontier approaches. The comparison of results according to the models may translate into higher reliability of the undertaken labour efficiency analysis in construction and its conclusions. Although the results are not characterized by high compatibility, the conducted analysis indicated the most attractive countries taking into account labour cost to profit and turnover ratios of enterprises. One of the determinants which should not be ignored when analysing the labour efficiency is the level of development of a country; however, it is not the sole factor affecting the efficiency of the sector.
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.
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