End-to-end deep meta modelling to calibrate and optimize energy consumption and comfort

TL;DR

This paper presents an end-to-end deep learning approach using recurrent neural network metamodels to optimize energy consumption and comfort in large buildings, achieving significant efficiency gains without renovations.

Contribution

It introduces a novel RNN-based metamodel trained on simulation data, calibrated with real building data, and integrated into multi-objective optimization frameworks.

Findings

Up to 10% energy savings achieved.

Metamodel is computationally efficient and adaptable.

Effective calibration with real building data.

Abstract

In this paper, we propose a new end-to-end methodology to optimize the energy performance as well as comfort and air quality in large buildings without any renovation work. We introduce a metamodel based on recurrent neural networks and trained to predict the behavior of a general class of buildings using a database sampled from a simulation program. This metamodel is then deployed in different frameworks and its parameters are calibrated using the specific data of two real buildings. Parameters are estimated by comparing the predictions of the metamodel with real data obtained from sensors using the CMA-ES algorithm, a derivative free optimization procedure. Then, energy consumptions are optimized while maintaining a target thermal comfort and air quality, using the NSGA-II multi-objective optimization procedure. The numerical experiments illustrate how this metamodel ensures a significant gain in energy efficiency, up to almost 10%, while being computationally much more appealing than numerical models and flexible enough to be adapted to several types of buildings.