Operational data analysis |
Above: HYDROGRID investment decision simulation
different maintenance windows, using the same high-resolution data used for production planning, ensuring consistency in decision-making.
4. Investment decision simulation: When considering plant upgrades or investments, HYDROGRID Insight provides data-driven simulations that help operators make informed decisions regarding investments in plant infrastructure, optimizing the long-term viability and performance of the plant.
What types of users within an organization typically interact with your platform? HYDROGRID Insight is designed to be used across different roles within the organization, with each user benefiting from the platform’s unique features. Production planners: These users benefit from forecasting tools, generation planning, and adherence to environmental regulations, both short- and long-term. Technicians: The maintenance planning feature is crucial for technicians, allowing them to optimize maintenance schedules and reduce downtime. Traders: Traders focus on water value across the fleet, using the platform to assess marginal prices and optimize bidding strategies. Financial decision makers (consultants, project developers, turbine manufacturers, and hydro O&Ms): these type of roles use HYDROGRID Insight’s investment simulation tools and dashboards to track financial performance, assess financial feasibility, optimize project planning, and support long-term investment strategies.
HYDROGRID Insight is accessible via a responsive website, meaning it can be used across a range of devices, including mobile phones and tablets. While the platform works on any device, it is optimized for larger screens due to the heavy use of data visualizations. On smaller screens, users may see a condensed view of the information, but it still functions seamlessly.
For the best experience, users are encouraged to access the platform on larger devices for an optimal interface, as the detailed dashboards and full plant overview are easier to navigate.
How is machine learning applied within HYDROGRID’s solutions to enhance forecasting accuracy? Machine learning is a powerful tool for uncovering correlations within data and predicting their effects. Its advantage lies in the ability to learn from data
20 | February 2025 |
www.waterpowermagazine.com
without the need for painstakingly simulating every process or creating explicit physical models, which can often be too complex to maintain. A prime example of this in HYDROGRID Insight is our inflow forecasting. Traditionally, inflow predictions would require detailed physical models of the landscape, water flow, and storage dynamics, but machine learning allows us to bypass these complexities. Instead, the system relies on available data – such as weather forecasts, rainfall, and historical inflow measurements – and the machine learning model identifies patterns and learns how past weather conditions influenced water levels and flow into the plant. It can then predict future inflows based on this historical data, without needing to model the physical landscape at all. This approach provides a more efficient and scalable solution to forecasting.
What challenges have you faced in integrating machine learning into hydropower operations, and how have you overcome them? Integrating machine learning models can be challenging, especially when the required data is sparse or inconsistent. One of the main challenges is that the more complex and powerful the model, the more data it requires to train effectively. In simpler terms, the better the model, the more “feeding” it needs to develop – just like how more intelligent species tend to take longer to mature and require more nourishment and experiences during development. To overcome this, it’s crucial to understand the typical data that operators have access to and choose the models accordingly. Not all hydropower plants have the advanced sensors or telemetry data, and this variability can impact a model’s performance. HYDROGRID Insight’s dedicated analytics team plays a key role in building these models to work with the customer’s specific data. They ensure that the models remain powerful yet practical for real-world applications, providing operators with accurate insights and predictions despite limitations in data availability.
How does your platform address inflow forecasting and incorporate these forecasts into operational planning? HYDROGRID Insight uses advanced signal processing and machine learning techniques to model inflow forecasting, turning it into a data- driven prediction problem. The generated inflow forecast is not just a black box – it’s displayed to users in a transparent manner so that they can understand and trust the forecast. This transparency builds confidence in the system and gives operators control over the decision-making process. The inflow forecast is then used as a core input to generate the optimized operational plan, ensuring that the plant operates efficiently according to forecasted water levels. This planning is dynamic, taking into account both current and predicted conditions to maximize energy production while adhering to constraints. Our platform works with customers across various climates – from continental to tropical regions – adapting the forecasting model to suit each environment’s unique characteristics.
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
