Energy conservation measurements in buildings are more and more popular as they benefit from an intelligent contractual framework called Energy Performance Contracts (EPC), where energy savings are measured as the difference between a predictive baseline model and the actual consumption. While modern predictive models make use of large amounts of data from external sources and increasingly complex algorithms, these two aspects make their use difficult in practice because they need mutual understanding and transparency, requiring the involvement of a third-party for auditing. In this sense, we designed and developed a prototype that overcomes these issues by storing the predictive models and the data in an immutable blockchained data structure using the Ethereum framework. To the best of our knowledge, this is the first working prototype using the blockchain technology applied to EPCs. This paper presents and discusses the technical solutions and best-practice guidelines adopted in this prototype.

An adaptive multiple subtraction step is necessary for almost all methods that predict seismic multiple reflected waves. We aim at giving a better understanding of matching filters based on l q -norms and on statistical independence. We found that the formulation of all of these techniques can be gathered in a mutual framework by introducing a space-time operator, called the primary enhancer, acting on the estimated primaries. The differences between the considered matching filters become more intuitive because this operator behaves as a simple amplitude compressor. In this perspective, all the methods tend to uncorrelate the predicted multiples and the enhanced estimated primaries. The study of these matching-filter methods can be narrowed to the study of the primary enhancer operator because it is the only difference. Moreover, we have emphasized the role of using adjacent traces or windowing approaches in terms of statistics, and we show that an adequate windowing strategy may overbear the choice of the objective function. Indeed, our analysis showed that setting a good windowing strategy may be more important than changing the classical leastsquares adaptation criterion to other approaches based on l q -norm minimization or independent component analysis.