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Abstract

There are many IT tools available on the market that carry out various types of forecasts in the gas industry. Programming evolves with the availability and capability of computers. IT tools support the user in engineering calculations, but also present the obtained results in an interesting visualization, e.g. in the form of interactive charts. The software can support making business decisions, which, in turn, can be used as business intelligence. In the era of digitization, huge metadata of measurements are created, so conducting data analyzes in the energy sector is very common. Moreover, rapidly evolving artificial intelligence creates new opportunities. The article presents a sample analysis of calculations using RStudio, an integrated development environment for the R language, a programming language for statistical calculations and graphics. The aim of the article is to present the possibility of using R language software to make a forecast and to determine the quality of forecasts. The article aims to present the possibility of making forecasts based on mathematical models available in R packages and the possibilities offered by the forecasting platform to readers. The article presents the U.S. market and has a particular focus on Natural Gas Residential Consumption in Pennsylvania (publicly available data from the U.S. Energy Information Administration). This dataset represents the monthly consumption of natural gas between 2015 and 2020. Forecasts were presented over a span of 12 months.
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Authors and Affiliations

Tomasz Chrulski
1
ORCID: ORCID

  1. Faculty of Drilling, Oil and Gas, University of Science and Technology AGH, Kraków, Poland
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Abstract

The heat supply systems energy efficiency improvement requires the use of increasingly complex methods. The basic ways to reduce heat consumption is by using better thermal insulation, although they have more and more limited possibilities and need relatively large financial outlays. Good effects can be achieved by the better heat source adaptation to the conditions of a specific facility supplied with heat. However, this requires research that identifies the effectiveness of such solutions as well as the tools used to describe selected elements of the system or its entirety. The article presents the results of tests carried out for a gas boiler room supplying heat to a group of residential buildings. The goal was to build a model that would forecast the day range in which the maximum gas consumption occurs for a given day. Having measurements of gas consumption in subsequent hours of the day, it was decided to build a forecasting model determining the part of the day in which such a maximum would occur. To create the model the random forest procedure was used along with the mlr (Kassambara) package. The model’s hyperparameters were tuned based on historical data. Based on data for another period of boiler room operation, the results of the model’s quality assessment were presented. Close to 44% efficiency was achieved. Tuning the model improved its predictive ability.

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Authors and Affiliations

Bogdan Nowak
Grzegorz Bartnicki
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Abstract

As is well known, gas consumption and its prices depends on many factors including local factors, geopolitics, the development of the gas transport infrastructure (including liquefied natural gas), distribution and extraction costs – for example unconventional deposits (e.g. shale gas). The global gas market depends primarily on the economic relations between large gas producers and importers e.g. US-China, Middle East/US – Russia etc. (Olayele 2015). In individual countries, the price is also dependent on concluded contracts and delivery directions. Also it should be mentioned that the gas consumption depends on weather conditions, type of day of the year (holiday, business day, month) and economic situation (Kosowski et. al 2010). What impact has the appearance of the COVID-19 epidemic had on the European natural gas market?

The analyzed research problem concerned, in particular, two areas: gas consumption and its prices, in selected European countries in the aspect of the spread of the COVID-19 epidemic with reference to historical data from 2016–2019. Seven European countries belonging to the European Network of Transmission System Operators for Gas (ENTSOG) were selected, for which the highest inland consumption of natural gas by country was observed in the last year. The countries are presented in order or consumption: Germany, the United Kingdom, Italy, France, the Netherlands, Spain, Poland. The data has been downloaded from transmission system operators (TSOs) for each of these countries.

Furthermore the article showed information about the dates of governments restrictions (lockdown), LNG contract volumes, injection/withdrawal volumes (storage).

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Authors and Affiliations

Tomasz Chrulski
ORCID: ORCID
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Abstract

Describing the gas boiler fuel consumption as a time series gives the opportunity to use tools appropriate for the processing of such data to analyze this phenomenon. One of them are ARIMA models. The article proposes this type of model to be used for predicting monthly gas consumption in a boiler room working for heating and hot water preparation. The boiler supplies heat to a group of residential buildings. Based on the collected data, three specific models were selected for which the forecast accuracy was assessed. Calculations and analyses were carried out in the R environment using “forecast” and “ggplot2” packages. A good quality of the obtained forecasts has been demonstrated, confirming the usefulness of the proposed analytical tools. The article summary also indicates for what purposes the forecasts obtained in this way can be used. They can be useful for diagnosing the correct operation of a heat source. Registering fuel consumption at a level significantly deviating from the forecast should be a signal to immediately diagnose the boiler room and the heat supply system and to explain the reason for this difference. In this way, it is possible to detect irregularities in the operation of the heat supply system before they are detected by traditional methods. The gas consumption forecast is also useful for optimizing the financial management of the property manager responsible for the operation of the boiler room. On this basis, operating fees or financial operations with the use of periodic surplus capital may be planned.

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Authors and Affiliations

Grzegorz Bartnicki
Bogdan Nowak

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