Article

OPTIMUS Analysis tools to process data and inference rules

Álvaro SiciliaGonçal CostaHaris DoukasNational Technical University of AthensEvangelos SpiliotisNational Technical University of AthensG. AnastopoulosNational Technical University of AthensAlfonso CapozzoliPolytechnic University of TurinVincenzo CorradoPolytechnic University of TurinAlice GorrinoPolytechnic University of TurinBorja TelladoTecnalia

Zenodo (CERN European Organization for Nuclear Research)repository2016en

ABI

Abstract

This deliverable summarizes the work carried out in Task 3.2 Data Mining Analysis. The purpose of this task is to develop prediction models to forecast the energy performance behaviour of buildings. Four prediction models have been devised which deal with renewable energy production, energy consumption, indoor temperature, and energy prices.The theoretical premises of the prediction models and their implementations are outlined. The methodological approach described for every model will serve as a guide to develop new prediction models.The prediction models are a component of the OPTIMUS DSS. The models make use of the semantic framework –developed within Task 3.1 Semantic Framework for Data Integration– to retrieve the data they need.

Topics

Energy Load and Power Forecasting Building Energy and Comfort Optimization Data Mining Algorithms and Applications

Identifiers

DOI: 10.5281/zenodo.20283956

Citations and references

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