The paper aims at comparing forecast ability of VAR/VEC models with a non-changing covariance matrix and two classes of Bayesian Vector Error Correction – Stochastic Volatility (VEC-SV) models, which combine the VEC representation of a VAR structure with stochastic volatility, represented by the Multiplicative Stochastic Factor (MSF) process, the SBEKK form or the MSF-SBEKK specification.

Based on macro-data coming from the Polish economy (time series of unemployment, inflation and interest rates) we evaluate predictive density functions employing of such measures as log predictive density score, continuous rank probability score, energy score, probability integral transform. Each of them takes account of different feature of the obtained predictive density functions.

Households are the most significant group of consumers in the municipal and household sector in

Poland. In 2010-2016, households consumed annually from 8.9 to 10.8 million Mg of coal (77-81%

share in this sector).

As of the beginning of 2018, seven voivodships in Poland have already introduced anti-smog resolutions,

one has its draft, three are considering introduction of such resolutions. In the face of introducing

anti-smog resolutions, the analysis of coal consumption by households was conducted for a situation

where anti-smog resolutions will be introduced in all voivodships in Poland.

A forecast of hard coal consumption by Polish households in 2017-2030 was presented in the article.

Two scenarios differentiated in terms of calorific value of coal were taken into account: (i) concerned coal

with a calorific value of 24 MJ/kg (min. Q for eco-pea coal: grain size 5.0-31.5 mm), (ii) – coals with

a calorific value of 26 MJ/kg (Q recommended for use by producers of class 5 boilers).

In the perspective of 2030, the largest decrease in hard coal consumption can be expected (jointly)

in the voivodships of Śląskie, Dolnośląskie, Opolskie and Lubuskie. Under the assumptions made, in

relation to 2016, it may be reduced by half and fall from 2.8 to the level of 1.4-1.5 million Mg. The

smallest decreases in consumption may occur (jointly) in the Małopolskie, Lubelskie, Podkarpackie and

Świętokrzyskie voivodships – decrease by 16-22% and fall from 2.6 to approximately 1.9-2.0 million Mg.

On a national scale, coal consumption may decrease from the current 10.4 (2016) to around 6.3-6.8 million

Mg (a decrease of 30-35%).

Despite the decrease in hard coal consumption in the 2030 perspective, one should expect an increase

in demand for high quality coal dedicated to modern boilers (usually pea assortments) as well as qualified

coal fuels (mainly eco-pea coal).

The essay presents an original application of using the coolhunting method to discover new trends in architecture and design. The ability to identify trends is tied in with the possibility of attaining an advantage over the competition with the use of new designs that can become hits on the market, gaining the favor of customers. The term coolhunting can be broadly defined as the pursuit of inspiration and the forecasting of the directions of development. Initially, the term was applied to fashion, but quickly spread to other spheres of activity, like music, the arts, lifestyle and finally, to architecture and design. The essay is a slightly altered and improved rendition of the author's article published in Zastosowania ergonomii. Wybrane kierunki badań ergonomicznych w roku 2014 . (ed. Charytonowicz J.), Publ. Polskie Towarzystwo Ergonomiczne PTErg, o/Wrocław, 2014, p. 289-304. The method outlined therein is the result of research conducted under the author's supervision at the Institute of Architecture and Spatial Planning of the Poznań University of Technology between the years 2012 and 2014.

Traffic related noise is currently considered as an environmental pollution. Paper presents results of multidirectional study attempting to serve urban traffic without the need to erect noise barriers interfering urban space. Initial concept of the road expansion included construction of 1000 m of noise barriers dividing city space. Improvement in the acoustic conditions after construction completion is possible due to the applied noise protection measures: vehicle speed limit, smooth of traffic flow, use of road pavement of reduced noise emission and the technical improvement of the tramway.

While personality is strongly related to experienced emotions, few studies examined the role of personality traits on affective forecasting. In the present study, we investigated the relationships between extraversion and neuroticism personality traits and affective predictions about academic performance. Participants were asked to predict their emotional reactions two months before they will get their results for one important exam. At the same time, personality was assessed with the Big Five Inventory. All the participants were contacted by a text message eight hours after that the results were available, and they were requested to rate their experienced affective state. Results show moderate negative correlations between neuroticism and both predicted and experienced feelings, and that extraversion exhibits a weak positive correlation with predicted feelings, but not with experienced feelings. Taken together, these findings confirm that extraversion and neuroticism shape emotional forecasts, and suggest that affective forecasting interventions based on personality could probably enhance their efficiencies.

The aim of the paper is to point out that the Monte Carlo simulation is an easy and flexible approach when it comes to forecasting risk of an asset portfolio. The case study presented in the paper illustrates the problem of forecasting risk arising from a portfolio of receivables denominated in different foreign currencies. Such a problem seems to be close to the real issue for enterprises offering products or services on several foreign markets. The changes in exchange rates are usually not normally distributed and, moreover, they are always interdependent. As shown in the paper, the Monte Carlo simulation allows for forecasting market risk under such circumstances.

Weather forecasting requires knowledge of the laws of atmospheric movement. Apart from classic fluid mechanics, we must consider the rotational motion of our planet, the differential heating of its surface through the absorption of solar radiation, as well as water evaporation and condensation processes.

Changes in capacity of water reservoir Cedzyna during its exploitation since 1972 till 2003 are presented in the paper. Analyses were based on cross sections of the reservoir’s basin from before its fulfillment (1967) and those measured with the echo sounder Ceeducer in 2003. Silting of reser-voir was predicted based on empirical methods. The volume of reservoir was found to decrease by 112.8 thousand m3 during 31 years of its exploitation and reservoir’s life span was assessed at 685 years. An error analysis was additionally made of calculating the surface area of a cross section at varying number of sounding sites. It was found that there was no need to note too many coordinates and depths and for the Cedzyna reservoir the distance between measurement sites up to 16 m was sufficient.

Covering a wide area by a large number of WiFi networks is anticipated to become very popular with Internet-of-things (IoT) and initiatives such as smart cities. Such network configuration is normally realized through deploying a large number of access points (APs) with overlapped coverage. However, the imbalanced traffic load distribution among different APs affects the energy consumption of a WiFi device if it is associated to a loaded AP. This research work aims at predicting the communication-related energy that shall be consumed by a WiFi device if it transferred some amount of data through a certain selected AP. In this paper, a forecast of the energy consumption is proposed to be obtained using an algorithm that is supported by a mathematical model. Consequently, the proposed algorithm can automatically select the best WiFi network (best AP) that the WiFi device can connect to in order to minimize energy consumption. The proposed algorithm is experimentally validated in a realistic lab setting. The observed performance indicates that the algorithm can provide an accurate forecast to the energy that shall be consumed by a WiFi transceiver in sending some amount of data via a specific AP.

In the article, mathematical modeling methods are used to study the main trends and macroeconomic determinants of the electric car market development in 2011–2018 on the example of the US. The determinants include economic (GDP), socio-economic (household income), energy (electricity use), and environmental (СО2 emissions) factors. The authors justify the role of electric transport in strengthening national energy security due to the transition to renewable energy technologies and the reduction of fossil fuel use. Based on the constructed linear regression equations, a weak relationship has been revealed between the number of electric vehicles sold and the environmental factor, which can be explained by the small share of electric cars in the US market. The formed multifactor linear model showed a positive impact of both the country’s GDP growth and electricity consumption increase on the number of electric vehicles sold. However, the rise in household incomes negatively influences market development due to insufficient consumer awareness of the electric transport operation benefits, an underdeveloped network of electric vehicle charging stations, etc. Based on the obtained multifactor model, the authors have built optimistic, optimal and pessimistic scenarios for the US electric vehicle market deployment for the next five years. In order to implement the most favorable scenarios, recommendations for market development factors’ management have been made. The results of the study can be used to improve public policy in the US transport and energy sectors, as well as in other countries to optimize the fuel and energy balance, strengthen the energy independence of states by developing clean transport and adapting the model to national specifics.

Coal production in 2018 increased by 3.3% and amounted to 7.81 million tons. Compared to 2010, it increased by 620 million tons. The structure of coal production in the world is very stable in the analyzed period of 2010–2018. Steam coal dominates in production with a share of 77%. Since 1990, the share of coal in the consumption of primary energy carriers has fallen by 3% in the global economy. In the EU, the share of coal in the consumption of primary energy carriers is more than twice lower than in the world, and in 2018 amounted to 13%. BP estimates the sufficiency of coal proven reserves based on 2018 data for the next 132 years. For oil and gas, they are estimated at 51 years. The decline in hard coal production in the European U nion can be dated almost continuously since 1990, which has decreased by 74%. In 2018, 74 million tons of coal were produced in the EU. In 2018, hard coal consumption in EU countries dropped to 226 million tons, i.e. by 20.6%.

In 2018, global trade in steam coal amounted to 1.14 billion tons. The situation in China is crucial for the international coal market. The slight change in the import policy of this country significantly affects the situation in international trade in steam coal. In 2019, coal prices (at Newcastle, Richards Bay, ARA ports) dropped by an average of 23 U SD/ton. The average decreases for these three indices were 33%. The prices of steam coal in the forecasts presented in the paper are under pressure of the falling demand.

The methane hazard is one of the most dangerous phenomena in hard coal mining. In a certain range of concentrations, methane is flammable and explosive. Therefore, in order to maintain the continuity of the production process and the safety of work for the crew, various measures are taken to prevent these concentration levels from being exceeded. A significant role in this process is played by the forecasting of methane concentrations in mine headings. This very problem has been the focus of the present article. Based on discrete measurements of methane concentration in mine headings and ventilation parameters, the distribution of methane concentration levels in these headings was forecasted. This process was performed on the basis of model-based tests using the Computational Fluid Dynamics (CFD). The methodology adopted was used to develop a structural model of the region under analysis, for which boundary conditions were adopted on the basis of the measurements results in real-world conditions. The analyses conducted helped to specify the distributions of methane concentrations in the region at hand and determine the anticipated future values of these concentrations. The results obtained from model-based tests were compared with the results of the measurements in realworld conditions. The methodology using the CFD and the results of the tests offer extensive possibilities of their application for effective diagnosis and forecasting of the methane hazard in mine headings.