Accurate Seasonal Weather Forecasts

Up to $30,000
Challenge under evaluation

Challenge overview


Can you provide a solution for more accurately predicting rainfall and temperature anomalies in advance? Enel Green Power needs to have precipitation and temperature forecast, on monthly and annual granularity, as accurate as possible 9-12 months ahead to be able to estimate the production of its hydropower assets. Currently, European countries, mainly Italy and Spain, are the areas where it is most difficult to accurately predict rainfall and temperature forecasts because they are less affected by oceanic phenomena that influence the climate globally.

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As a result of the acute drought phenomena that have occurred in recent years, the production of Enel Green Power’s hydropower assets has fluctuated greatly from the forecast. This phenomenon has occurred mainly in Italy and Spain.

The models currently used to forecast rainfall start from oceanic phenomena, such as El Niño and La Niña, which act at the tropical Pacific level and then impact globally, influencing forecasts around the world. European countries however, especially those in the Mediterranean area, are less affected by such phenomena, due to geomorphological factors which are difficult to consider. These factors reduce the correlation between oceanic phenomena and rainfall in Mediterranean Countries, making it more difficult to accurately predict rainfall and forecasts in Italy and Spain – as well as their subregions.

Therefore, your proposal for a forecasting model must be able to accurately predict rainfall and temperature anomalies over a time horizon of about 9-12 months especially for Italy and Spain, and sub-regions within Italy and Spain. The proposed model would ideally also be able to predict acute drought or flood events with the same forecast horizon (9-12 months ahead).

Enel Green Power is looking for a forecasting model that, unlike those currently used, can give more accurate forecasts with a time horizon of 9-12 months ahead, especially for the Italy and Spain perimeter, which are not well predicted by the currently used models. Your model will help Enel Green Power to better manage water volumes, continue to provide renewable energy from hydropower assets, and help to avoid resource scarcity in the event of extreme weather conditions.

This Challenge provides a contribution to the followingSustainable Development Goals: 

  • SDG 7: Ensure access to affordable, reliable, sustainable, and modern energy for all.
  • SDG 9: Industry, Innovation, and Infrastructure.


Hydroelectric assets are required to be planned for production 9-12 months ahead, like a traditional source plant. Fundamental elements for correct program planning consist of the weather forecast and, in particular, rainfall and temperature forecasts. The last few years have seen, above all in Italy and Spain, the alternation of dry seasons followed by extreme acute effects and floods.

Medium-long term forecasts, especially in European countries, are still very unreliable today, so much so that the most accurate value that can be used is given by the historical average. However, the meteorological anomalies of recent years have led to a strong deivation from this value as well, making it difficult to make reliable estimates.

The development of an advanced forecasting model could finally make it possible to predict even acute events, in order to align the production estimates of hydroelectric plants.



Enel Green Power is looking for proposals for innovative models capable of providing accurate and reliable medium-to-long term (9-12 months ahead) forecasts of rainfall and temperature, especially in the Mediterranean area and Italy and Spain. Enel has looked at traditional climate models and meteorological processes internally, including the aptness of the NMME and ECMWF.

Enel is looking for a model that uses 'different/disruptive' approaches and variables. The model may use different kinds and blends of approaches (i.e. statistical, or physical, or machine learning modelling). The forecast must be passed according to macro-areas of homogenous climate conditions. We very much encourage innovation in this Challenge, for instance, your solution may take the form of a hybrid model using elements of traditional models combined with an innovative approach, or AI-backed methods for accurately predicting rainfall and temperature 9-12 months ahead.


Technical Criteria

Across your solution, system, or proposal, Enel  Green Power requires certain levels of data and consistent formatting for use in forecasting planning of hydroelectric assets in the mid-to-long term. Your proposal must:

  • Use historical data available for the last 20-30 years (if possible, but not exclusively using the ERA5 database),
  • Present the MAE (Mean Absolute Error) analysis related to these correlation anomalies and compare it with the MAE obtained using the simple historical mean,
  • Your correlations and MAEs must be compared with data obtained through the standard seasonal models such as the North American Multi-Model Ensemble (NMME), European Centre for Medium-Range Weather Forecasts (ECMWF), etc.
  • The minimum time step used can be the month.



Your submission, including your model and validated data, must provide Enel Green Power an estimation of the rainfall and temperature one year ahead in Italy and Spain:

  • Give an estimation of rainfall/precipitation 9-12 months ahead, in millimetres/day on monthly and annual granularity,
    • Present the correlation anomalies of precipitation values between the real anomaly (difference between the real and the mean value) of precipitation and the forecasted anomaly (hindcast). It must be done for a test period of at least 5 years, 2018-2022 included
  • Give an estimation of temperature ranges 9-12 months ahead, in degrees Celsius, on monthly and annual granularity,
    •  Present the correlation anomalies for temperature values between the real anomaly of temperature and the forecasted anomaly, again across a test period of at least 5 years, 2018-2022 included
  • Provide a locally executable application to verify the data or model for Enel Green Power to use,
  • Forecast model should report on a monthly and annual basis,
  • Provide data for the Italy and Spain perimeter, labelled, according to macro-areas of homogenous climate conditions – wide area predictions, rather than day-to-day weather forecasts,
  • The model for rainfall and temperature forecast should provide a percentage confidence level not above 5%.



The submitted proposal should consist of a detailed technical description including:

  • Forecasting model or system for temperature and rainfall that meets the Technical Criteria and Solution Requirements, with a particular focus on and accuracy around the regions of Italy and Spain;
  • Locally executable application to verify the solution;
  • Detailed description of results, performances and characteristic of the proposed model solution, particularly compared to the current models;
  • Data, case studies, patents and journal references or any additional material that supports the proposed solution or model.


For questions about the Challenge and your proposal you can contact

Explain your proposal clearly in English, and attach documents (max 5 files, 25MB total size, ZIP, JPG, PDF format) if needed.

This is a Reduction to Practice Challenge that requires written documentation, experimental proof-of-concept data, and prototype delivery for the purposes of evaluating the effectiveness of the solution.

To receive an award, the Solvers will not have to transfer the exclusive IP rights to the Seeker. Instead, Solvers will grant to the Seeker a non-exclusive license to practice their solutions.

Submissions to this Challenge must be received by 11:59 PM (Central European Time) on July 07, 2023

Late submissions will not be considered.

After the Challenge deadline, the Seeker will complete the review process and make a decision with regards to the Winning Solution(s). All Solvers that submit a proposal will be notified of the status of their submissions.


ABOUT THE SEEKER             

Enel is a multinational energy company and one of the world’s leading integrated electricity and gas operators. Enel works in more than 30 countries across five continents, generating energy with a total installed capacity of more than 89 GW, selling gas and distributing electricity across a network spanning approximately 2.2 million km.

With almost 75 million end users around the world, Enel has the biggest customer base among our European competitors, and we are one of Europe’s leading energy companies by installed capacity.

Enel Green Power (EGP) is the Enel Group company focused on the development and management of renewable energy generation. EGP is a world leader in the development and management of energy production from renewable sources with a global presence in 26 countries in Europe, the Americas, Asia, Africa and Oceania (active projects in 21 countries while projects under development in 5 countries). EGP operates more than 1,200 plants with a managed capacity of 56 GW across a generation mix that includes wind, solar, geothermal and hydroelectric and is at the forefront of integrating innovative technologies into renewable energy power plants. EGP enables communities, companies and end users to move towards a sustainable life, guiding the transition towards a decarbonised society and contributing to the development and well-being of the many geographical areas in which it operates. EGP aims to accelerate the energy transition towards renewable sources, aiming to reach 75GW of installed renewable capacity in 2025 worldwide. In parallel, the abandonment of coal by 2027 and gas by 2040 is also foreseen.

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