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In recent years, Poland has experienced a significant increase in the installed capacity of solar and wind power plants. Renewables are gaining increasing interest not only because of Poland’s obligations to European Union policies, but also because they are becoming cheaper. Wind and solar energy are fairly-well investigated technologies in Poland and new reports are quite frequently added to the existing research works documenting their potential and the issues related to their use. In this article, we analyze the spatial and temporal behavior of solar and wind resources based on reanalysis datasets from ERA5. This reanalysis has been selected because it has appropriate spatial and temporal resolution and fits the field measurements well. The presented analysis focuses only on the availability of energy potential/resources, so characteristics intrinsic to energy conversion (like wind turbine power curve) were not considered. The analysis considered the last 40 years (1980–2019) of available data. The Spearman coefficient of correlation was considered as a complementarity metric, and the Mann–Kendal test was used to assess the statistical significance of trends. The results revealed that: The temporal complementarity between solar and wind resources exists mostly on a seasonal scale and is almost negligible for daily and hourly observations. Moreover, solar and wind resources in joint operation exhibit a smoother availability pattern (assessed based on coefficient of variation). Further findings show that the probability of ‘resource droughts’ (periods when cumulative generation was less than arbitrary threshold) lasting one day is 11.5% for solar resources, 21.3% for wind resources and only 6.2% if both resources are considered in a joint resource evaluation. This situation strongly favors the growth of local hybrid systems, as their combined power output would exhibit lower variability and intermittency, thus decreasing storage demand and/or smoothing power system operation.

Office buildings play a significant role in shaping the current electricity demand and its trends. Their energy demand patterns impact the power system operation on a national and regional level. What is more, both (office buildings and the power system) are also simultaneously influenced by meteorological parameters. Considering the above, the aim of this paper is to analyze a three-year hourly energy demand time series recorded in a relatively large office building in Kraków (Southern Poland). This paper will fill a gap in the literature as there is a lack of evidence from Central European countries in the area of office buildings' energy demand and its relationship with meteorological parameters. The data was obtained from a local electricity provider whereas meteorological parameters came from weather station and satellite measurements. The analyses focused on determining the typical weekly and daily demand patterns as well as on investigating the correlation between meteorological parameters (wind speed, irradiation, humidity, and air temperature) and observed energy consumption. To estimate the correlation between investigated variables, a Pearson coefficient of correlation was used. For distinguishing typical load patterns, a k-means clustering method was applied. The relationship between meteorological parameters and load was also tested based on multiple linear regression analysis. The results indicated that energy demand had a relatively strong positive correlation with irradiation and with temperature and a negative one with humidity. The correlation with wind speed was not greater than 0.25. Dividing the data into three subsets shows that energy demand generally exhibits a stronger correlation with meteorological parameters on working days. Additionally, clustering analysis has shown that it is possible to distinguish three typical daily patterns of energy demand and meteorological parameters that correspond to a hot/warm day, cold days and days that are intermediary between those two. The regression analysis showed that meteorological parameters can explain/model a significant part of the load variability (up to 50%) although the quality of such models is relatively poor (in terms of mean absolute percentage error the best model exhibited a value of 16%). The results of this study can be used as a benchmark for similar office buildings that received the same level of sustainability certification, or in the future analysis of climate change impact on power demand.

This paper presents an algorithm for modeling electricity and natural gas consumption in a walking furnace with the use of artificial intelligence and simulation methods, depending on the length of the rolling campaign and the established rolling program. This algorithm is the basis for the development of a proposal for a set of minimum requirements characterizing the Best Available Techniques (BAT) for beam furnaces intended for hot rolling, taking into account the requirements set out in national regulations and the recommendations described in the BREF reference documents. This information should be taken into account when drawing up an application for an integrated permit, as well as when setting emission limit values. Based on the constructed algorithm, it was shown that depending on their type and technical specification, the analyzed projects will offer measurable economic benefits in the form of reducing the amount of energy consumed by 1,076,400 kWh during the implementation of 50 rolling campaigns to reduce gas by 14,625 GJ and environmental benefits in the form of reduction of pollutant emissions into the atmosphere 80–360 g/Mg. The constructed algorithm was validated in the Dosimis-3 program, based on a discrete event-driven simulation. Thanks to this representation of the model, its user can interactively participate in changes that take place in the model and thus evaluate its behavior. The model, verified in real conditions, can be the basic source of information for making effective operational technological decisions related to the preparation of production at the rolling mill as part of planning and long-term activities.

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