Projects

W4E: Weather 4 Energy & Infrastructure

Weather forecasting to inform energy and infrastructure

Funding: ICSC Innovation Funds

Enabling technology: Advanced Modeling and Simulation, Artificial Intelligence, High Performance Computing, Machine Learning

Sustainable Development Goals

The central role played by weather forecasts in informing the energy sector and, more broadly, the infrastructure system, is far from matched by an ideal qualitative and quantitative interpretation of their downstream impacts.

This project’s purpose addresses exactly this gap. The consortium will develop an innovative framework to generate accurate, granular forecasts and future climate projections for underlying energy and infrastructure systems. Examples of these products include wind or photovoltaic power generation at specific sites, changes in power line ampacity across Italy, and future scenarios simulating stress on the road network and electrical systems.

To achieve these goals, the project will use statistical techniques that build a quantitative and flexible approach. The expected impacts on Italian infrastructure are manifold. First, the final products are expected to have a high TRL (Technology Readiness Level), giving stakeholders the ability to use the quantitative tools in a semi-operational mode. These statistical tools are highly flexible and scalable, so their application to other use cases and/or sectors is also anticipated.

National Centre for HPC, Big Data and Quantum Computing (ICSC), a project funded by the European Union – NextGenerationEU – and by Italy’s National Recovery and Resilience Plan (PNRR) – Mission 4, Component 2.

Objectives

The project aims to develop a weather model that transforms weather information into impact forecasts for the Italian energy system. The main goal is to improve the planning and resilience of the Italian energy system with respect to climate change and its possible impacts.

The project provides accurate forecasts and simulations for use in a wide variety of fields, from short-term photovoltaic and wind power generation to power line capacity, as well as modeling hydrogeological phenomena and monitoring road infrastructure under future climate scenarios.

Initial challenge

The project’s challenge is to develop an innovative framework that efficiently translates weather information into impact information, in order to obtain comprehensive and accurate forecasts and simulations.

Solution

Using machine learning and artificial intelligence techniques, the project provides accurate forecasts and simulations across different time scales, from short- to long-term. This approach allows for a more comprehensive and detailed assessment of the challenges and opportunities associated with the growing reliance on renewable energy and the effects of climate change. The solution aims to improve the planning and resilience of the energy system, enabling more informed and timely decisions.

Benefits

Accurate, high-resolution weather forecasts can help optimize electricity distribution, thereby enabling informed market decisions for bidding. Over short time horizons, such as those covered by this project, accurate weather forecasts for generation can help asset owners and market operators such as Eni Plenitude make better bids in electricity markets.

For electricity system operators such as Terna, accurate short-term weather forecasts for renewable energy generation can improve unit commitment (operational planning of generation units) and operational planning, increase distribution efficiency, reduce reliability issues, and thus minimize the amount of operating reserves needed in the system.

Further benefits can be found in relation to public infrastructure, such as highways. In this case, the impacts are mainly related to developing a data analysis model to improve knowledge of the state of the territory near highway assets, with the aim of improving the ability to prevent events that pose risks to people’s safety and infrastructure integrity; the ability to plan maintenance operations; and the ability to plan investments in existing parts of the infrastructure (e.g., monitoring) or in new candidate segments of the highway network.

IFAB’s role

IFAB contributed to developing the methodologies and analytical tools needed to transform weather information into impact forecasts for the Italian energy and infrastructure system, supporting operational planning and strengthening resilience to climate change.

Its contribution focused on four complementary areas:

Developing statistical methodologies for a quantitative and flexible approach to generating accurate forecasts and future climate projections.
Designing models and simulations to estimate the effects of weather and climate conditions on specific use cases: wind and photovoltaic production at specific sites, power line capacity, hydrogeological phenomena, and road infrastructure monitoring.
Defining analytical pipelines capable of converting weather information into impact indicators directly usable in decision-making processes.
Delivering technologically mature products, designed to be scalable and reusable across additional use cases and sectors.

Participation in W4E allowed Fondazione IFAB to consolidate advanced expertise at the intersection of statistical modeling, climate analysis, and artificial intelligence applied to energy and infrastructure systems, built within the context of a partnership of excellence.