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.
