| AI & machine learning
(FERC) driving significant changes to safety standards and regulations. Balancing energy production and regulatory compliance has always presented a significant burden on the operator, and emerging gaps in water forecasting present further challenges to operators looking to refine the decision making between when to produce power, while balancing compliance with social, ecological and environmental regulation. To protect third-party property, operators must conduct regular inspections, maintenance, and safety evaluations to mitigate the risk of flooding that can damage private properties downstream. Compliance requires maintaining specific stream flows to preserve local ecosystems. Erratic water releases can disrupt habitats, endanger species, and degrade water quality. Recreational activities, which rely on stable predictable water levels, such as fishing, may also be impacted. Regulatory frameworks mean that operational needs must be balanced with consideration for activities in the area and accurate water forecasting plays a key role in achieving this.
The bridge to a smarter future AI learning tools have already demonstrated their
capability in comparable meteorological contexts where they have outperformed the accuracy of established forecasting models more than 90% of the time, using smaller, more widely available hardware. AI models provide high-resolution forecasting, which works by collecting weather from different sources to simulate atmospheric conditions at a fine scale. These targeted forecasts offer the opportunity for more efficient resource allocation and water management. Precedents for translating this performance exist and show clear benefits as well as the rapid return on AI investment available for the hydropower industry. AI forecasts are starting to see uptake amongst the major traditional forecasting organisations – such as the European Centre for Medium-Range Weather Forecasts (ECMWF) – as the pre-eminent technology firms unveil their own proprietary technology.
Managing flood and storm events While the adoption of AI for weather forecasting is still
embryonic, hydropower operators are embracing the technology for the management of flood and storm events. This advanced water forecasting provides insights which enable operators to implement proactive measures that reduce infrastructure damage and downstream flooding. AI can generate forecasts at the basin watershed or plant level, providing targeted insights for precise water management that understand local hydrological dynamics and inform accurate predictions. To date, AI has already been used to manage water resources in complex watersheds with diverse climatic conditions, resulting in significant reduction in operational risks related to water variability, achieving improvements in forecast precision and decision-making.
Protecting construction and O&M Alongside the management of storm surge events,
operators are continually working to safeguard their ongoing construction projects, as well as routine operations and maintenance (O&M), from unexpected water level changes. Confidence in the reliability
www.waterpowermagazine.com | September 2024 | 15
and accuracy of AI-based predictions enables the scheduling of works to occur during safer periods, which reduces risk and enhances safety. AI water forecasting applied in the Pacific northwest region has demonstrated the ability to optimise reservoir management and improve energy production and carries the additional benefit of enhanced environmental compliance. In Europe, where reservoirs have smaller capacities, the windows to act on insights related to changeable water volumes are compressed, and AI’s real-time processing improves decision making capabilities.
AI has also been used successfully in the identification of suitable locations for dam construction by analysing environmental, geographical, and social data. The Nature Conservancy, along with computer scientists, has used AI to evaluate hundreds of existing and proposed hydropower sites across the entire Amazon basin, to find sites with minimal ecological disruption and eliminating the expense of moving or cancelling operations. Operators must comply with regulations like those set by FERC, which require maintaining specific reservoir levels. AI-based tools help by simulating different meteorological models and predicting water level changes using Machine Learning algorithms. These methods allow operators to manage water flows and quality effectively, ensuring compliance.
A sustainable future In its 2024 World Hydropower Outlook, the International
Hydropower Association advocates for harnessing data analytics, machine learning, and predictive maintenance algorithms. Adopting these tools gives “operators deeper insights into plant performance, water management, and condition monitoring.” AI-based water forecasting and modelling are
leveraging advanced machine learning algorithms, real-time data processing, and climate adaptation models, to provide precise and actionable insights that support efficient, safe, and sustainable hydropower management. These technologies support water level predictions, scheduling optimisation, and safeguard regulatory compliance for the hydropower industry, ensuring a route to a sustainable future for hydropower.
Below: AI-based water forecasting and modelling tools can help support efficient, safe, and sustainable hydropower management as the climate crisis intensifies
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53
