Power capacity profile estimation for building heating and cooling in demand-side management

- We present a new methodology to estimate power capacity profiles.
- We use a classification approach to estimate the capacity.
- Our methodology works with an existing demand-side management module.
- We take advantage of the structure of the problem.
- We report the performance of our approach on a real-world-based scenario.

This paper presents a new methodology for the estimation of power capacity profiles for smart buildings. The capacity profile can be used within a demand-side management system in order to guide the building temperature operation. It provides a trade-off between the quality of service perceived by the end user and the requirements from the grid in a demand-response context. We use a data-fitting approach and a multiclass classifier to compute the required profile to run a set of electric heating and cooling units via an admission control module. Simulation results validate the performance of the proposed methodology under various conditions, and we compare our approach with neural networks in a real-world-based scenario.