Analytics for energy efficiency concepts and applications

Panel: 4. Undertaking high impact actions: The role of technology and systems optimisation

Authors:
Véronique Boutin, Schneider Electric, France
Rodolphe Héliot, Schneider Electric, France

Abstract

Incredible amounts of data are created, shared and stored every day, but companies are just beginning to harness its potential. The next step will consist in leveraging this data in order to improve the efficiency of industry, infrastructure or buildings. With Analytics, a new range of information and knowledge becomes accessible: data can be transformed into KPIs, can be benchmarked, or can be used in order to build patterns and models. Thanks to this new information, better action and decision making for the short-term can be made, better rationale for future design & investment strategies can be used, and better asset management schemes can be derived.

The goal of the paper is to explain how Analytics contribute to improve Energy Efficiency in industrial contexts:

– By producing new information such as forecast of energy consumption, forecast and benchmark of local production and energy costs, allocation of energy and costs to usages, and detection of energy wastage;

– By enabling better action through optimized planning, scheduling and control of single -or multi- source energy production, storage, consumption and trading;

– By providing rationale for design & evolution strategy, through investment planning for energy production or recovery facilities, through energy contract portfolio optimization or energy price risk management.

A practical example will be given in the field of lift/hoisting machines, where analytics are used for optimized design and control of a multi-sources energy system (including energy storage), leveraging energy recovered from braking and energy coming from local photovoltaic panel in a valuable and robust way. As a conclusion, the paper will provide a vision of Analytics as seen by Schneider Electric, relying on 7 technical clusters that provide the foundations for an efficient design of solutions. We will show how this set of Analytics methods can cover the huge diversity of applicative needs.