@ARTICLE{Nowak_Bogdan_Forecasting_2019, author={Nowak, Bogdan and Bartnicki, Grzegorz}, number={Nr 109}, journal={Zeszyty Naukowe Instytutu Gospodarki Surowcami Mineralnymi Polskiej Akademii Nauk}, pages={93-110}, address={More info at Journal site: https://min-pan.krakow.pl/wydawnictwo/czasopisma/zeszyty-naukowe-instytutu-surowcami-mineralnymi-i-energia-pan/ https://min-pan.krakow.pl/wydawnictwo/wp-content/uploads/sites/4/2018/02/Wskazowki-ZN-ang-2018.pdf}, howpublished={online}, year={2019}, publisher={Instytut Gospodarki Surowcami Mineralnymi Polskiej Akademii Nauk}, 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.}, type={Artykuły / Articles}, title={Forecasting the time interval of the day with the maximum boilers gas consumption}, URL={http://czasopisma.pan.pl/Content/113652/PDF/Nowak-Bartnicki.pdf}, doi={10.24425/znigsme.2019.130166}, keywords={gas consumption, prognostic model, random forest}, }