Classification of Buildings Energetic Performance Using Artificial Immune Algorithms

摘要

The building sector is responsible for a large share of Europe's energy consumption. Modelling buildings thermal behavior is a key factor for achieving the EU energy efficiency goals. Moreover, it can be used in load forecasting applications, for the prediction of buildings total energy consumption. The first phase of this work is the application of Artificial Immune Systems (AIS) for clustering buildings with similar physical characteristics and similar thermal efficiency. In the second phase, Artificial Neural Networks (ANN) are used to estimate the buildings heating and cooling loads. A final sensitivity test is performed to identify which building features have the most impact on the heating and cooling loads. The results obtained in the first phase revealed very distinct cluster prototypes, which demonstrates the AIS discriminating ability. The good estimation performance obtained in the second phase showed that this approach can be integrated in energy efficiency audits. Finally, the sensitivity analysis provided indications for actions (or legislation directives) in order to promote the design of more efficient buildings.