Energy information systems, which manage energy consumptions over internet, have been evolving over the past decade and can be considered as a part of a specialised sequential decision process, regarding the provision of personalised energy services to the community. The aim of this study is to develop and present an innovative decision-support system and cloud computing software methodology that brings together energy consultants, consumers, energy services procedures and modern web interoperable technologies. The authors propose a web-based knowledge system, using distributed cloud architecture and metering grids over ADSL broadband connections. By using some clustering algorithms and a web middleware, energy profiles over time are analysed and observed. The resulting clusters and centroids are projected and statistically analysed over time, producing a centroid-locus. Hypercube topology was used for efficient data management and software agent-based parallel analysis. The system operates efficiently on a multi-tier cloud-based middleware that generates in real-time using various service software components to the end consumers. The case study on real Greek energy measurements, for the first time in Greece, indicated a compact and efficient distributed procedure that could analyse and produce adaptive personalised information services.