ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

Best Manager Award (Alberto Pasanisi) and Young Statistician Award (Antonio Canale)

12 September 2017, 17:10 – 18:10

Statistical Methods and Tools as Drivers of Innovation in the Energy Industry: Some Insights and Examples

Alberto Pasanisi (EDF R&D - EIFER)

Modern engineering and business require undoubtedly more and more multidisciplinary skills. As a heavy trend in the current practice of engineering, it is worth highlighting the increasing use of computer simulation and the exploitation of more and more huge and heterogeneous sources of data. In the end, today’s engineers need to complete and to update constantly their toolbox with tools coming from the domains of information technology and applied mathematics.
In particular, statistics and data analysis provide valuable methods and tools to quantify and manage uncertainties when forecasting the behaviour of industrial or natural systems, to extract knowledge from data and expertise and to recommend decisions: actually, they help solving a consistent part of problems, today’s engineering is concerned with.
Inspired from the feedback of more than 15 years in the field of advanced engineering and R&D, this talk highlights some examples of the use of statistical methods to implement innovative solutions for industrial problems, with a particular focus on the energy industry.
Without any pretention of exhaustiveness, the examples cover a great variety of energy-related activities (reliability of components and structures, natural hazards, energy efficiency, smart and sustainable cities ...), and put into evidence the cross-disciplinary and crucial role played by statistical methods.

Constrained Functional Time Series: An Application to Demand and Supply Curves in the Italian Natural Gas Balancing Platform

Antonio canale (Università di Padova)

In Europe several legislative and infrastructural measures have been undertaken to regulate energy markets. In Italy, for example, we have assisted to the recent introduction of the natural gas balancing platform, a system in which gas operators virtually sell and buy natural gas in order to balance the common pipelines network. Basically, the operators daily submit demand bids and supply offers which are eventually sorted according to price. Demand and supply curves are hence obtained by cumulating the corresponding quantities.

Motivated by market dynamic modelling in the Italian natural gas balancing platform, we propose a model to analyze time series of monotone functions subject to an equality and inequality constraint at the two edges of the domain, respectively, such as daily demand and offer curves. In detail, we provide the constrained functions with a suitable pre-Hilbert structure and introduce a useful isometric bijective map associating each possible bounded and monotonic function to an unconstrained one. We introduce a functional-to-functional autoregressive model that is used to forecast future demand/offer functions. We estimate the model via minimization of a penalized mean squared error of prediction with a penalty term based on the Hilbert-Schmidt squared norm of autoregressive lagged operators.
The approach is of general interest and is suited for generalization in any situation in which one has to deal with functions subject to the above constraints which evolve through time.

 

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