Typical load profiles in the smart grid context – a clustering methods comparison

The present research paper presents five different clustering methods to identify typical load profiles of medium voltage (MV) electricity consumers. These methods are intended to be used in a smart grid environment to extract useful knowledge about customer’s behaviour. The obtained knowledge can be used to support a decision tool, not only for utilities but also for consumers. Load profiles can be used by the utilities to identify the aspects that cause system load peaks and enable the development of specific contracts with their customers. The framework presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partition, which is supported by cluster validity indices. The process ends with the analysis of the discovered knowledge. To validate the proposed framework, a case study with a real database of 208 MV consumers is used.

Application of the nZEB methodology in the retrofitting of a typical Portuguese dwelling from the 60's.

Buildings and the whole built environment are in a key role when societies are mitigating climate change and adapting to its consequences. More than 50% of the existing residential buildings in EU-25 were built before 1970. Thus, these buildings are of significant importance in reducing energy consumption and CO2 emissions. The existence of more nearly zero energy buildings (nZEB) is a possible solution for this problem. This study aims to analyze the application of the nZEB methodology in the retrofitting of a typical Portuguese dwelling build in 1950. It was shown that the primary energy used can be reduced to a very low value (11,95 kWhep/m2.y) in comparison with the reference consumption (69,15 kWhep/m2.y), with the application of the best construction techniques together with the use of energy from on-site renewable sources.