Using Artificial Intelligence to Predict Power Demand in Small Power Grids—Problem Analysis as a Method to Limit Carbon Dioxide Emissions

Tomasz Ciechulski, Jacek Paś, Marek Stawowy () and Stanisław Duer
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Tomasz Ciechulski: Institute of Electronic Systems, Faculty of Electronics, Military University of Technology, 2 Gen. S. Kaliski St., 00-908 Warsaw, Poland
Jacek Paś: Institute of Electronic Systems, Faculty of Electronics, Military University of Technology, 2 Gen. S. Kaliski St., 00-908 Warsaw, Poland
Marek Stawowy: Department of Air Transport Engineering and Teleinformatics, Faculty of Transport, Warsaw University of Technology, 75 Koszykowa St., 00-662 Warsaw, Poland
Stanisław Duer: Department of Energy, Faculty of Mechanical Engineering, Koszalin University of Technology, 15–17 Raclawicka St., 75-620 Koszalin, Poland

Sustainability, 2025, vol. 17, issue 8, 1-20

Abstract: The article discusses the application of advanced data mining methods applicable to electricity consumption within a local power system in Poland. This analysis involves power demand. It is aimed at predicting daily demand variations. In such a case, system demand is characterized by high variability over a short period of time, e.g., 24 h. This constitutes a significant issue within a small power grid. It entails effective load programming on a given day and time. Therefore, the authors of the paper suggested employing artificial intelligence to forecast industrial power grid load for successive time intervals of the operation process. Such a solution applied within a power system enables appropriate start-up/shut-down planning, as well as generator operation at a specific capacity in power plants. It thus allows continuous power system (on-line) load demand balancing. Predicting power system load also involves determining moments, e.g., of power plant start-up, transition times to maximum or minimum output, or also the shut-down of such a process. This means ongoing and continuous (automatic) impact on electricity distribution. It significantly reduces carbon dioxide atmospheric emissions and allows zero-emission, e.g., wind, hydro, geothermal, or solar plants to meet current power needs. The issue associated with operating small ‘island’ power systems is a dynamic and rapid change in power demand. This is related to the area-based—‘island’—use’ of available power sources that can only be operated within a specific area. A very important problem occurring within these structurally small grids is the continuous forecasting of load changes and real-time response to power demand (i.e., balancing power demand through in-house or available power sources).

Keywords: electricity consumption; prediction; artificial intelligence; neural networks; LSTM; coal-fired power plants; gas emissions; atmospheric pollution (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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