In the recent past, several examples of the application of Exergy Analysis (ExA) to entire Countries have been published, and it can be fairly said that -while the goals of the individual Authors were completely consistent- the results, the conclusions and the recommendations seem to diverge. There are several contingent reasons for this, but the underlying problem is that a purely thermodynamic analysis does not represent the complex influence that monetary, social, political and technological factors have on the purely “material” or “energetic” streams. Clearly, ExA represents a substantial improvement with respect to the “Material and Energy Balance Reports” published annually by most industrialized Countries, because the exergy flow diagram unequivocally demonstrates how and at what penalty the primary exergy inflow (fossil fuels, renewables, ores, harvested food and feed)is transformed into final energy, such as diesel fuel, electricity or other commodities. The issue here is that the so-called Externalities (Capital, Labour and Environmental Effects) are, in spite of some opinion to the contrary, completely left out of the picture. It turns out though that ExA can be extended by including the exergy equivalents of the externalities: the theory is called Extended Exergy Accounting (EEA) as a reminder of the inclusion of monetary, labour and environmental “exergy costs” in the global budget. Scope of the study presented in this paper is twofold: first, to discuss in a formally structured fashion a general procedure for performing the EEA of a “System Country”; second, to inspect the results to identify possible correlations between the “exergy efficiency” of a Country -i.e., its ability to exploit the incoming exergy flux with the minimum amount of losses (material, energy waste and irreversible exergy destruction)- and some of the most popular indicators used to assess the “degree of sustainability” of a system, bearing in mind a time frame of ten years (2007-2017).As an efficiency indicator we use the Exergy Footprint, a measure of the global primary exergy consumption of a system, which turns out to be also a very useful indicator of the “degree of unsustainability” of a Country. Our results confirm that the Gross National Product, already criticized for not representing the reality of the economy of a Country, is very weakly -if ever- correlated with the EF in the decade of observation. Other indicators display different degrees of correlation with the EF, and the reasons are critically analyzed.

The motivation for this article was an effort to create an battery electric drive of a smaller excavator of a well-known manufacturer. This research followed the solution of the kinetic energy recovery of the road roller. The aim of the excavator electrification research was to replace its internal combustion engine with an electric motor so that the innovated excavator does not burden its surroundings with gas emissions and excessive sound emissions, so that it can work in confined spaces or protected areas. The solution was carried out in a number of stages– a patent search, the structure, functions, variants of individual solutions were analyzed, the technical possibilities of individual subsystems and the whole proposed electric drive system and the entire excavator with its mechanical and hydraulic systems were analyzed. The resulting variant included an accumulator, a Bodas RC control system, an electric motor control, a frequency converter, a special electric motor, etc. 3D models of drive elements were created to deal with the installation. Simulation models of electrical and hydraulic parts of the drive were created to select the most suitable solutions, which were verified or predicted by simulations in Matlab/Simulink environment. Tests have shown that the excavator is capable of operating for at least 7 hours without recharging the battery. The knowledge gained within the project will help in the electrification of other constructions, earthmoving and other machines, specifically the wheel loader, etc. In the development of the new electric battery drive of the excavator, mathematical modeling and simulations have proved to be a valuable tool. The main achieved results of the project are a functional model of a zero-emission mini-excavator with significantly reduced noise, a proven control algorithm in the form of software and its utility model according to the application 2018-35127 adopted by the Industrial Property Office of the Czech Republic. Innovation of the solution of the excavator was awarded the Gold Medal at the International Engineering Fair in Brno in 2018.

Burundi is facing to low access to electricity and low quality of supply. The electricity sector relies on hydroelectric plants and high transmission lines constructed in the decade of 1980. The connection extension increased the number of customers without increasing the installed capacity. The electricity sector is managed by a public company, which is at the same time in charge of the production, the transport and the distribution of electricity, the pumping, the treatment and the supply of drinking water in the main centers urban and secondary centers. Since the beginning of the decade of 2000, the government of Burundi edicted policies and textbooks related to the electricity sector, aiming to liberalize and regulate the sector, in order to increase the access to electricity and the quality of supply. This paper makes a review of policies drivers, reform and organization of electricity sector in Burundi through the different national policies, strategies, laws and decrees. We estimate the performance of the electricity sector using the descriptive statistics. We discuss the results using semi-structured interviews carried with officials from the ministry responsible for energy, the Régie de Production d’Eau et d’Electricité (REGIDESO) and electricity users. Results show that few steps of reform were realized in electricity sector. Also, access to electricity remains low, while the quality of service is poor. We recommend attracting more investment in electricity generation and stabilize the national economy to increase electricity consumption.

In this century, with increasing society’s population and Gross Domestic Product (GDP) trend, energy demand is increased in the countries of the whole world. Nowadays, the use of different Renewable Energy Sources (RESs) in the network has become commonplace and, of course, has been challenged. In this way, forecasting energy demand plays a key role in the development of different parts of a country. In this study, firstly a prediction of consumption and fluctuations in the sources of energy is made, and secondly, regarding different parts of the industry, agriculture, and households, two different scenarios have been analyzed to provide this demand in the future. An Artificial Neural Network (ANN) method has been used to predict energy consumption level, and also the two factors of the increase in population and GDP have been considered. Prediction of population increase rate, with respect to its statistical complexities, is derived from a literature review of other references; the GDP prediction is derived with a conventional method of the Grey method. Then, with the prediction of the aforementioned factors, energy consumption is predicted by a metaheuristic algorithm. Afterwards, scenarios related to the energy consumption are predicted and priorities are given, such as environmental impacts, in order to provide the predicted consumption level. Scenarios will considerably show that the supply and demand should be managed by fossil fuel energy production replaced with RESs in the supply side, and providing products with higher energy efficiency in the demand side.

The purpose of the work is to determine factors internal and external affecting the cooling energy demand of the building. During the research, the impact of weather conditions and the level of hotel occupancy on cooling energy, which is necessary to obtain indoor comfort conditions, was analyzed. The subject of research is energy consumption in the Turówka hotel located in Wieliczka (Southern Poland). In the article, the designer of neural networks was used in the Statistica statistical package. To design the network, a widely-used multilayer perceptron model with an algorithm with backward error propagation was used. Based on the collected input and output data, various MLP networks were tested to determine the relationship most accurately reflecting actual energy consumption. Based on the results obtained, factors that significantly affect the consumption of thermal energy in the building were determined and a predictive energy demand model for the analyzed object was presented. The result of the work is a forecast of cooling energy demand, which is particularly most important in a hotel facility. The prepared predictive model will enable proper energy management in the facility, which will lead to reduced consumption and thus costs related to facility operation.