A framework for prediction and segmentation of daily energy load profiles of building clusters using machine learning
Abstract
In the future smart grid and smart city context it is a necessary step to predict load profiles and categorize them for energy system management although the focus of existing studies on the energy profile classification and customer segmentation is still mainly on historical data analysis. In this paper, we propose a framework to identify and classify daily energy load profiles, and further categorize the forecasted future load profiles. The contributions of this study lie in: (i) A two-step clustering method for load profiling and categorization preserving both consumption magnitude and the shape of the load profile. (ii) A comprehensive classification procedure enabling the classification of forecasted daily heating profiles for the day ahead demand side management.
Info
Other-Internet Publication, 2020
UN SDG Classification
DK Main Research Area
Science/Technology
