Artificial Intelligence and Digitalisation: A New Chapter for Hydropower in Latin America

<p><em>How the Inter-American Development Bank and HYDROGRID are piloting AI-driven optimisation to modernise hydropower operations in Honduras and Costa Rica</em></p>

<p>Latin America's hydropower fleet, the backbone of the region's electricity supply, faces a dual challenge: aging infrastructure and a rapidly evolving grid. As variable renewables reshape power systems, hydropower must transition from a baseload workhorse to a flexible, responsive complement to wind and solar. This paper examines how digitalisation, and AI-powered inflow forecasting in particular, can drive that transition. Drawing on the Inter-American Development Bank's (IDB) perspective on hydropower modernisation and a new pilot programme deploying HYDROGRID's optimisation platform in Honduras and Costa Rica, the authors argue that digital tools are no longer optional – they are a technical and economic necessity for the region's hydropower future.</p>

<h2>1. The imperative for hydropower modernisation in Latin America</h2>

<p>Hydropower remains the single largest source of renewable electricity worldwide, with an installed fleet of approximately ~1,440 GW (including pumped storage)^{<a href="#ref3">3</a>,<a href="#ref6">6</a>}, according to IHA's <em>World Hydropower Outlook</em> for 2025 and <em>REN21 Global Status Report 2025</em>. Roughly one-third of that capacity is more than 40 years old^{<a href="#ref5">5</a>}. In Latin America, the proportion is even more significant: the region depends on hydropower for nearly half its electricity^{<a href="#ref1">1</a>,<a href="#ref4">4</a>}, and many of its largest plants were commissioned in the 1970s and 1980s^{<a href="#ref4">4</a>}. These assets were designed for a different era – one in which hydro-dominated grids operated under predictable baseload regimes with relatively straightforward dispatch.</p>

<p>That world no longer exists. The rapid integration of wind and solar generation across the region has fundamentally altered the role hydropower must play. Plants are now required to manage more frequent start-stop cycles, faster ramping, and extended operation beyond their original design parameters, all while demand continues to grow. These new operating conditions accelerate mechanical wear and increase the urgency of modernisation.</p>

<p>The economic case is compelling. Modernising existing hydropower capacity is significantly cheaper and faster than building new greenfield projects, and it carries far fewer environmental and social trade-offs. As one industry perspective frames it: the longer we extend the lifetime and energy output of an existing project, the better its total environmental footprint. Modernisation is not merely a smart investment – it is an essential one.</p>

<h2>2. IDB's approach: digitalisation as a pillar of modernisation</h2>

<p>At the Inter-American Development Bank, modernisation is understood as an umbrella term covering any intervention in a power plant that involves replacing or upgrading equipment. This can range from replacing turbines and generators to overhauling electromechanical control systems with modern digital controls. Critically, the IDB's view is that digitalisation – including advanced analytics, forecasting tools, and optimisation platforms – is itself an integral component of modernisation, not a separate initiative.</p>

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<p>"We usually argue that you shouldn't modernise a power plant without digitalising it," explains Arturo Alarcón, Senior Energy Specialist at the IDB. "If you're intervening in the power units, you should address the rest as well – even dam safety. The entire hydropower complex should be considered holistically." This holistic perspective is informed by hard economics: a large hydropower plant taken offline for a year can lose USD 10 to 15 million in foregone generation. If operators do not address all possible improvements during a planned outage, they risk additional costly shutdowns in subsequent years for interventions that could have been bundled into the original programme.</p>
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<p>What is changing the calculus, according to Alarcón, is the evolving role of hydropower within Latin American power systems. "Hydropower used to operate as baseload in predominantly hydro-thermal systems, which were relatively easy to manage and predict. Now, with more wind and solar integration, hydropower is no longer baseload – it's a complement to variable renewables," he notes. "You can't efficiently manage a plant with highly variable output without advanced control systems and short-term forecasting tools. Digitalisation is no longer just 'adding value' – it's a technical necessity."</p>

<p>From a financing perspective, the IDB regards digitalisation as a positive signal – a "green flag" – when evaluating modernisation proposals. Conversely, cybersecurity gaps and cultural resistance to digital adoption are red flags. Alarcón emphasises that digitalisation is as much a cultural shift as a technological one: if an organisation focuses only on technology and not on people – their training, mindset, and adaptation – the project is likely to underperform. Turning plant operators into digital champions, rather than viewing them as obstacles, is essential to long-term success.</p>

<h2>3. The IDB-HYDROGRID pilot: AI-powered optimisation in Honduras and Costa Rica</h2>

<p>In early 2026, the IDB selected HYDROGRID, a Vienna-based hydropower software company, to implement its AI-driven optimisation platform in a smart-hydro digitalisation project spanning two countries. The initiative is supported by Empresa Nacional de Energía Eléctrica (ENEE) in Honduras and Instituto Costarricense de Electricidad (ICE) in Costa Rica. It encompasses several hydro plants and represents one of the first large-scale deployments of an integrated digital optimisation platform for public-sector hydropower in Central America.</p>

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<p>The project deploys HYDROGRID Insight, a cloud-based platform that combines advanced analytics and machine learning to support key operational decisions. The deployment includes several interconnected modules: inflow and reservoir forecasting, production planning, long-term generation planning, and opportunity-cost calculation for maintenance scheduling. Taken together, these tools aim to maximise generation uptime and efficiency through data-driven decision-making – replacing the manual, experience-based approaches that have traditionally governed hydropower operations across the region.</p>

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<p>"This pilot is about more than technology," says Alarcón. "It's about building smarter systems that strengthen hydropower's role in sustainable development. By integrating digital tools into planning and operations, ENEE and ICE can improve efficiency, reduce waste, and deliver reliable clean energy to millions."</p>
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<p>The pilot is designed to demonstrate tangible results across three dimensions: optimising the use of available water through better inflow forecasting, planning maintenance windows to minimise revenue loss, and enabling longer-term strategic planning that accounts for hydrological variability and market dynamics. If successful, the model could serve as a blueprint for scaling digital hydropower solutions across the IDB's portfolio in Latin America and the Caribbean.</p>

<h2>4. Inflow forecasting: the foundation of smarter water management</h2>

<p>Among the suite of digital tools available to hydropower operators, inflow forecasting occupies a unique and foundational position – particularly in Latin America. The region's hydrological regimes are characterised by high seasonal and inter-annual variability, complex river basin topographies, and, in many cases, limited or fragmented monitoring infrastructure. These conditions make accurate inflow prediction both exceptionally challenging and exceptionally valuable.</p>

<p>Inflow forecasting underpins virtually every other operational decision at a hydropower plant. Without a reliable understanding of how much water will be available and when, operators cannot optimise reservoir levels, schedule generation to capture peak-value hours, or coordinate maintenance without risking shortfalls. In systems with cascading plants along the same river, the compounding effect of forecast uncertainty multiplies: an error at an upstream plant propagates downstream, affecting the entire chain.</p>

<p>HYDROGRID's approach to inflow forecasting uses machine learning models trained on historical hydrological data, meteorological inputs, and satellite-derived information. The models generate short-term forecasts (hours to days) and medium-term outlooks (weeks to months) that feed directly into the production planning and water management modules. This integration is critical: a forecast is only as useful as the decisions it enables. By embedding inflow predictions within an end-to-end optimisation framework, operators can translate hydrological insight into concrete operational actions – such as adjusting turbine and gate dispatch schedules, modifying reservoir draw-down strategies, or rescheduling planned outages.</p>

<p>In the context of the Honduras and Costa Rica pilot, inflow forecasting takes on added significance. Both countries face pronounced wet and dry seasons, and their hydropower systems must navigate periods of abundance and scarcity with limited storage flexibility. For run-of-river plants, which constitute a significant share of the installed capacity, accurate short-term forecasting is especially critical: every cubic metre of water that spills over a weir instead of passing through a turbine represents lost generation and lost revenue.</p>

<p>The broader regional context reinforces the importance of this capability. Climate change is altering precipitation patterns across Latin America, making historical averages less reliable as planning benchmarks^{<a href="#ref2">2</a>}. AI-driven forecasting models can adapt to shifting hydrological regimes more rapidly than traditional statistical methods, learning from new data as it becomes available. This adaptive capacity will become increasingly important as the gap between historical norms and observed conditions continues to widen.</p>

<h2>5. From pilot to scale: a pathway for the region</h2>

<p>The IDB-HYDROGRID pilot is significant not only for its immediate operational objectives but also for the precedent it sets. Latin America's hydropower sector has been historically conservative in its adoption of digital technologies – a posture that, as multiple industry voices have noted, is understandable given that these are critical infrastructure assets. However, the barriers to entry have dropped considerably. Modern sensor technology has become cheaper and easier to install; cloud-based platforms eliminate the need for on-site computational infrastructure; and the analytical tools to extract value from operational data have matured to a point where deployment is no longer experimental.</p>

<p>As Alarcón observes, digitalisation need not wait for a major overhaul. Even relatively new plants, those only 10 to 15 years old, can benefit from digital interventions that do not require long shutdowns. Installing modern sensors, connecting to cloud-based analytics platforms, and implementing forecasting tools can deliver meaningful improvements in performance and reliability with modest capital expenditure. This low barrier to entry means there is a viable pathway for digitalisation across the entire installed base – not only for the oldest plants facing end-of-life decisions, but for mid-life assets that have decades of operation ahead of them.</p>

<p>The human dimension is equally important. Successful digitalisation requires a change management approach that prepares people, aligns incentives, and builds internal capacity. The goal, in Alarcón's framing, is to consider the entire hydropower complex holistically – not just one turbine, but the full system, from the watershed to the grid connection point, and the people who operate it. This systems-level perspective is what distinguishes a modernisation programme that delivers lasting value from one that simply installs new equipment.</p>

<p>Looking ahead, the signals from both the financing and operational sides are converging. Development banks are increasingly seeking evidence of digital readiness in the projects they fund. Operators are recognising that data-driven decision-making can unlock efficiency gains that were previously invisible. And the energy transition itself is demanding a more agile, responsive role for hydropower – one that cannot be fulfilled without the tools that digitalisation provides.</p>

<h2>6. Conclusion</h2>

<p>The modernisation of Latin America's hydropower fleet is not a question of whether but of how – and how quickly. Digitalisation, anchored by AI-powered inflow forecasting and integrated optimisation platforms, offers the most cost-effective and impactful pathway to extend the life, improve the performance, and redefine the role of the region's most important renewable energy source. The IDB-HYDROGRID pilot in Honduras and Costa Rica demonstrates that this vision is not theoretical: it is already being deployed, tested, and refined in the field.</p>

<p>For the thousands of hydropower plants across Latin America that will need to be modernised in the coming decades, the lessons from this initiative may prove as valuable as its technology. Modernisation works best when it is holistic, when it brings people along, and when digital capability is built in from the start rather than bolted on after the fact. The future of hydropower in the region will be shaped by those who act on that understanding today.</p>