Energy cost assessment and forecasting

by | Nov 7, 2022

Application Track:

Ready Made

Code:

REACH-2022-READYMADE-SMAT_1

Domain:

Proposed by:

SMAT

Entity Logo:

Summary of the entity:

SMAT is the water utility managing the integrated water supply and wastewater service in the whole Metropolitan City of Turin (in the Piedmont region, north-west of Italy), for more than 2.2 million inhabitants from almost 300 municipalities. These include Turin urban area, which accounts for about half of the total population served. SMAT Group is a leader in the field of integrated water services and operates in the areas of engineering, construction and management of diversified water sources, state-of-the-art drinking water treatment systems, drainage systems and recycling of urban wastewater, collection, purification and recycling networks, energy cogeneration and recovery systems.

Since 2008, SMAT has strongly engaged in research activities, with the inauguration of the Research Centre counting 7 full-time researchers and about 50 operators with a variety of competences including engineering, chemistry, physics, biology and biotechnology and has a fully equipped laboratory. Currently, SMAT Research Centre is involved in about 20 projects (two EU-funded Horizon 2020 projects).

Summary of the challenge:

 In this challenge we are looking for a tool to evaluate costs related to energy consumptions based on energy market prices.

Description:

In this period, the expense for the energy consumption is becoming increasingly more important, as the energy prices have significantly risen, and it impacts directly on costs and revenues of the company. Moreover, due to the volatility of the energy market, the billing of energy consumption will follow an hourly updated price (related to National Single Price – PUN).

In order to approve the billing of the energy consumption, it is mandatory to check that the hourly energy price correctly matches the corresponding amount of energy consumed in the related hour. 

The dataset will be composed of 1500 records (any of these corresponding to a plant) related to a whole year consumption (represented by 8.760 entries each). The other input data are the hourly energy prices over the selected reference year. 

The challenge is to develop a tool capable of:

  • Estimating the expense for a selected month providing the previously mentioned inputs with graphical overview and summary related to the energy price variation.
  • Providing a model that compares a short-term energy consumption period (e.g., the first three months of the current year) with a historical energy consumption profile (e.g., the whole past year), evaluating the deviation (increasing energy demand or energy saving policies for each point of delivery).
  • Furthermore, it will forecast the impact on expenses depending on different future market prices evolution.

Data:

500+ set of records, any of these composed by 8760 hourly energy consumption data (kWh) referred to 2021;
1500+ set of records, any of these composed by 5831 hourly energy consumption data (kWh) referred to jan-aug 2022.
8760 hourly energy market prices referred to 2021 from GME website;
5831 hourly energy market prices referred to 2022 from GME website + 2929 hourly energy market prices referred to 2022 form EEX Futures market;
8760 hourly energy market prices referred to 2023 form EEX Futures market

Expected outcomes:

Data-driven approach to analyse energy consumption, using table, graphs and/or dashboards to visualise results and optimisation possibilities. The development of the application using open-source tools is a plus.

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