ENEL OPEN INNOVABILITY® CHALLENGE | Accurate seasonal weather forecasts

30/05/2023 - Enel

Being able to better predict the weather forecast associated to scarcity/abundancy of rainfall and temperature values, sufficiently in advance (9-12 months), has become a crucial factor in enabling proper utilization of water resources and gaining a competitive advantage from good asset planning.


Expires: 30 June 2023

Reward: Up to $30,000


Meet the challenge!


egp-accurate-weather


Abstract

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.


OVERVIEW

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 floods 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.


Description

SCENARIO

Hydroelectric plants are a key part of the future of renewable energy, accounting for approximately 40% of total renewable capacity according to the World Economic Forum. These plants, often complex, are subject to energy production variability due to rainfall.


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 events 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 deviation from this value as well, making it difficult to make reliable estimates.


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


THE CHALLENGE

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. In particular, this Challenge relates to the existing difficulty in predicting weather and climate events in Italy and Spain, except for the very short term.


Enel Green Power has looked at traditional climate models and meteorological processes internally, including the aptness of the North American Multi-Model Ensemble (NMME), European Centre for Medium-Range Weather Forecasts (ECMWF), etc. Forecast Centers the world over have extensive infrastructure designed to predict rainfall, temperatures, concentration of both, and acute weather events. Since the hereabove mentioned models didn’t provide reliable/representative results for our specific purposes, in particular for 9-12 months ahead mainly in Italy and Spain, Enel is looking for a model that uses "different/disruptive” approaches and variables, different from those normally used.


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. Solvers may leverage these existing forecasts or ensembles in their solution, but must be able to demonstrate the value added by their model, relative to any input datasets or foundational frameworks, for better chance of a full award.


This forecast model must be capable of predicting and quantifying seasonal rainfall and temperature at least 9-12 months ahead, on monthly and annual granularity, to allow the inclusion of these forecast values in the energy production models used by Enel Green Power. Furthermore, the model would ideally be able to predict acute events and prolonged periods of drought, in which the traditionally monitored quantities can deviate from the historical average values.



More info at openinnovability.com, meet the challenge!





Tipologia: Challenge prize

Fonte: https://openinnovability.enel.com/challenges/call/2023/5/innovative-models-weather-forecasts

Paese: Brasile, Messico, ITALIA, Svizzera, Spagna, Australia, Norvegia, Repubblica Ceca, Costa Rica, India, Kenya, Regno Unito, Danimarca, Cina, Germania, Cile, Sudafrica, Serbia, Francia, Finlandia, Austria, Giappone, Vietnam, Federazione Russa, Paesi Bassi, Egitto, Argentina, Singapore, Malesia, Lituania, Svezia, Israele, Canada, Corea del Sud, Stati Uniti d'America, Belgio

SSD: 01 - Scienze matematiche e informatiche, 02 - Scienze fisiche, 03 - Scienze chimiche, 04 - Scienze della terra, 05 - Scienze biologiche, 06 - Scienze mediche, 07 - Scienze agrarie e veterinarie, 08 - Ingegneria civile e Architettura, 09 - Ingegneria industriale e dell'informazione, 10 - Scienze dell'antichità, filologico-letterarie e storico-artistiche, 11 - Scienze storiche, filosofiche, pedagogiche e psicologiche, 12 - Scienze giuridiche, 13 - Scienze economiche e statistiche, 14 - Scienze politiche e sociali