Národní úložiště šedé literatury Nalezeno 1 záznamů.  Hledání trvalo 0.01 vteřin. 
Combined heat and power production planning in a waste-to-energy plant using machine learning
Kollmann, Marek ; Miklas, Václav (oponent) ; Touš, Michal (vedoucí práce)
This research deployed machine learning to optimize day-ahead production planning in Waste-to-Energy (WtE) plants, grappling with issues like noisy data, uncontrollable external consumption, and fluctuating steam production due to waste as a fuel source. The primary aim was to accurately predict the power transferred to the grid, which was achieved by creating a comprehensive model consisting of seven sub-models in cascade. Each sub-model was critically evaluated using standard metrics like R2 and Mean Relative Error. Findings revealed a significant improvement in prediction accuracy, resulting in more balanced production plans and reduced operational penalties. The approach led to an estimated annual increase of power delivered by 13% and profit by 2.6 million CZK for a specific plant.

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