Data Analytics applied in the electric sector has been leveraged in the recent years, primarily owing to the introduction worldwide of Automatic Meter Reading/Management and smart grids technologies operated by electric Utilities. Utilities have to face with return of investment from this infrastructure and at the same time have to fulfil the promise of providing better and innovative service to the customer, retailer and distribution network. This paper deals with the innovative SHAPE Web software platform for Data Analytics applied to the load patterns sourced from the Italian Enel network's smart meters. A previous contribution reported on the customer classification and segmentation modules implemented in the SHAPE platform. This work describes the Load prediction and Non-technical losses modules. The SHAPE Datawarehouse (DW) currently stores four years of progressively updated customer's load patterns

SHAPE: The load prediction and non-technical losses modules

Les sciences experimentales font regulierement usage de series chronologiques, pour representer certains des resultats experimentaux, qui consistent en listes chronologiques de valeurs (indexees par le temps), generalement fournies par des capteurs relies a un systeme (objet de l’experience). Ces series sont analysees dans le but d’obtenir un modele mathematique permettant de decrire les donnees et ainsi comprendre et expliquer le comportement du systeme etudie. De nos jours, les technologies de stockage et analyse de series chronologiques sont nombreuses et matures, en revanche, quant au stockage et a la gestion de modeles mathematiques et leur mise en lien avec des donnees numeriques experimentales, les solutions existantes sont a la fois recentes, moins nombreuses et moins abouties. Or,les modeles mathematiques jouent un role essentiel dans l’interpretation et la validation des resultats experimentaux. Un systeme de stockage adequat permettrait de faciliter leur gestion et d’ameliorer leur re-utilisabilite. L’objectif de ce travail est donc de developper une base de modeles permettant la gestion de modele mathematiques et de fournir un systeme de « requete par les donnees », afin d’aider a retrouver/reconnaitre un modele a partir d’un profil numerique experimental. Dans cette these, je presente donc la conception (de la modelisation des donnees, jusqu’a l’architecture logicielle) de la base de modeles et les extensions qui permettent de realiser le systeme de « requete par les donnees ». Puis, je presente le prototype de la base de modele que j’ai implemente, ainsi que les resultats obtenus a l’issu des tests de ce-dernier.

https://tel.archives-ouvertes.fr/tel-01939430/document

Conception et exploitation d'une base de modèles : application aux data sciences

Distribution system operators will gather an increasing amount of electricity data from the lower level of the grid to be able to cope with several challenges, such as an increase of distributed, heterogeneous energy production, local storage, and electric vehicles. Most DSOs are not yet prepared for collecting, storing and processing these large amounts of data. This paper introduces a method for designing, validating and implementing such a system. The most important aspect of this method is the fact that the domain expert is able to validate the constructed conceptual model, before it is used to create a working system. This validation step adds to the quality of the model, and therefore to the resulting system. We have applied our method to a use case where a DSO researcher wants to answer questions such as “Can we recognize which appliances are present in households?” and “How can we cluster similar households?”.

Applying innovative IT modelling methods to low-level grid information for DSO operations

This paper intends to present the main benefits that the Enel AMI solution is delivering to distribution companies of the group and to other utilities. One of the advantages in operating an AMI is provided by the availability of data concerning the consumption and the energy flows. Since 2005 Enel has been working to design and develop the proper tools to support the revenue protection everyday operations. Data alone are not enough to increase operations efficient and deliver better results in terms of revenue protection. Therefore the data are analyzed by systems and presented to the final users in form of reports. The track record in revenue protection can be considered really satisfactory, both for Enel group distribution companies and customer utilities. Nevertheless Enel continues to test new solution; in the paper we would like to provide some insights on the new trend in data analysis and pattern recognition. SHAPE (Statistical Hybrid Analysis for load ProfilE) is an innovative Web software platform for Business Analytics applied to the load patterns sourced from the Enel network’s smart meters. The SHAPE platform enables the Data Analyst to solve tasks such as hourly energy analysis, customer classification, load prediction, and non-technical losses detection.

Enel experience in revenue protection: ami solution delivered benefits and shape, business analytics platform, preparing for next years' challenges

This paper deals with an innovative Web software platform for Business Analytics applied to the load patterns sourced from the Enel network’s smart meters. Using ad hoc developed advanced time series analysis, the SHAPE (Statistical Hybrid Analysis for load ProfilE) platform enables the Data Analyst to solve in a user-friendly Rich Internet Applications (RIA) important tasks such as hourly energy analysis, customer classification, load prediction, and non-technical losses detection. The platform, developed within a R&D project, represents a tangible example of Applied Research-Industry knowledge transfer. By means of a Web Service interface, the platform exposes and shares the implemented models with other Corporate or thirdparty applications. The SHAPE datawarehouse currently stores three years progressively updated of load patterns for about 100,000 measurement points at the customers’ premises.

/pdf/shape-a-new-business-analytics-web-platform-for-getting-319r2nsvmu.pdf

SHAPE: A new Business Analytics web platform for getting insights on electrical load patterns

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SHAPE: A new Business Analytics web platform for getting insights on electrical load patterns