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About the Project
Electricity prices are constantly increasing, and household consumer demand response is a field gaining more interest. This allows the user to understand the behaviour of power consumption and therefore bring down the costs of energy by monitoring what is increasing the demand for energy. This project proposes a method that involves an algorithm that is able to forecast the electricity consumption in a household and classify what appliance is consuming more electricity at various points in time. This concept includes intelligently collecting data and processing it near the user, applying the notion of Edge Computing (collection and processing of data at the Edge, near the end-user). The methodologies used in the project consist of AI methods (i.e. RNN) and the concept of Dynamic Time Warping (DTW). Data collection is completed using Sundance Lynsyn Lite Sensor (Djupdal, et al., 2020) (Sundance, 2023)
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