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IT Expo. AIDI 2005-01: Capstone Term II. Submitted by: Group No.7 Bavithra Ganesan (100900119) Jasmeet Kaur (100881373) Pritesh Dalal(100872247) Rutvik Shah(100886648).
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Problem Statement. This study aims to investigate the viability of applying data mining algorithms for energy consumption forecasting in industrial settings, specifically in the context of a South Korean steel manufacturing facility. The study uses cutting-edge methods including machine learning algorithms and artificial neural networks to forecast daily energy use patterns. The main objective of this study is to assess the precision and efficacy of these models in predicting energy consumption and to find possible areas where energy may be saved, operational efficiency can be increased, and useful insights can be provided for practical applications..
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Model Architecture. Graphical user interface Description automatically generated.
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Modelling Techniques. Linear regression models the relationship between a dependent variable (energy consumption) and independent variables using a linear equation..
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Results. Chart bar chart Description automatically generated.
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Future Directions. E xpanding the analysis to more locations and business sectors to evaluate how well the data mining models can be applied broadly. Analysing the underlying causes of energy use more thoroughly to find areas where energy can be saved, and efficiency can be increased. A nalysing the effectiveness of the data mining models across extended time spans to determine their applicability in actual energy management scenarios..
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THANK YOU.