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Deep learning-based power baseline modelling of a range of electrical loads in smart green buildings

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dc.contributor.author Gunawardhana, KVSD
dc.contributor.author Lakshitha, WHAS
dc.contributor.author Perera, ULDE
dc.date.accessioned 2024-07-19T03:41:15Z
dc.date.available 2024-07-19T03:41:15Z
dc.date.issued 2023-12
dc.identifier.uri http://dl.lib.uom.lk/handle/123/22576
dc.description.abstract Energy consumption modelling of electrical loads plays a crucial role in modern-day energy management systems for green commercial buildings. This project focuses on the development of power baseline modelling using deep learning techniques for a diverse range of electrical loads. The baseline model, an estimation of power or energy consumption before implementing energy management, is widely used to identify savings by comparing with the measured data after implementing energy management. Energy efficiency is crucial for both commercial and non-commercial applications. Power baseline models give reference to identify energy saving or loss. We can assess energy saving after implementing energy conservation strategies and identify energy wastage of the system when actual power consumption is higher than the power baseline model prediction. In this study specifically, a comparison is made between Karl's Pearson's and Random Forest-based deep learning approaches and Recurrent Neural Network (RNN) models. This project incorporates both simulations and real-world data to conduct the study. en_US
dc.language.iso en en_US
dc.publisher Engineering Research Unit en_US
dc.subject Power Baseline en_US
dc.subject Deep Learning en_US
dc.subject Neural Network en_US
dc.subject Energy Management en_US
dc.subject Abnormalities en_US
dc.title Deep learning-based power baseline modelling of a range of electrical loads in smart green buildings en_US
dc.type Conference-Extended-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Department of Electrical Engineering en_US
dc.identifier.year 2023 en_US
dc.identifier.conference ERU Symposium - 2023 en_US
dc.identifier.place Sri Lanka en_US
dc.identifier.pgnos pp. 36-37 en_US
dc.identifier.proceeding Proceedings of the ERU Symposium 2023 en_US
dc.identifier.email salithadulshan@gmail.com en_US
dc.identifier.email sachinwickramasinghe97@gmail.com en_US
dc.identifier.email dumindueranga@gmail.com en_US
dc.identifier.doi https://doi.org/10.31705/ERU.2023.17 en_US


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