HYDROGRID Insight |
Machine Learning in Inflow Forecasting
How hydro producers can harness the power of machine learning to enhance operational efficiency
IN TODAY’S COMPETITIVE energy market, hydropower producers need more than just raw data – they need actionable insights. To gather these, smart inflow predictions are crucial, as they help minimize imbalance costs by learning from the past to forecast the future. Solutions like HYDROGRID Insight simplify the process of collecting, structuring, and organizing data into accurate predictions of both historical and future reservoir inflows.
Leveraging data for strategic
decisions Having the ability to foresee water inflow accurately is a tough job without the right technology at hand, but also a key competitive advantage in the current energy markets. The ability to foresee water inflows allows hydro plants to make informed strategic decisions, optimizing production weeks or even months in advance. This proactive planning capability is especially beneficial for hydroelectric plants with limited storage flexibility, providing a substantial edge even in short- term scenarios.
Tailored machine learning
forecasting models Generating insights from data involves more than processing information; it requires addressing various water management needs – from immediate emergency responses to strategic decisions for up to a year ahead. A single forecast model often cannot meet all these requirements. Instead, a suite of tailored models working in tandem is ideal. HYDROGRID has spent nearly eight years perfecting their
Hourly granularity ‘Cockpit View’ featuring synergies between energy price, production planning, reservoir levels and inflow
models to handle real-time data errors and deliver robust performance. Their technology helps power producers in 10 countries, among which Dalane Kraft, SSE Renewables, RWE, or Småkraft.
HYDROGRID Insight employs multiple machine learning forecast models, each tailored to different time scales and specific hydro basins. By analyzing historical data and weather forecasts, these models are fine-tuned for each plant in HYDROGRID’s extensive portfolio, keeping the balance between tailored and scalable.
Machine learning for long-term
vs. short-term forecasts Consider a hydro-energy plant with significant storage capacity. For medium- and long-term planning (weeks or months ahead), inflow forecasts must account for seasonal patterns, snow coverage, and long-term trends. These forecasts grow more accurate with each new data point added.
Conversely, short-term forecasts, or ‘nowcasts’ (one to 48 hours ahead), are essential for day-ahead and intra-day trading. These nowcasts, which rely on localized rain events and real-time catchment area developments, are updated as frequently as every hour, offering higher precision through telemetry ‘actuals’ rather than historical patterns.
Real-time data management Managing these forecasts in real-time is a
complex task. For hydropower producers, keeping inflow forecasts aligned with weather fluctuations can require tens to hundreds of daily forecasts. HYDROGRID’s hydro-tailored solution automates and customizes the collection, structuring, and interpretation of raw telemetry data, ultimately computing real-time inflow forecasts ready when needed, where needed. By leveraging machine learning and advanced
forecasting solutions, hydropower producers can transition from reactive to proactive planning, ensuring efficient and optimized operations in an increasingly competitive energy market. Tools like HYDROGRID Insight help operators by displaying the link between actuals, forecasts and planning for both water inflow and energy prices (see above). Hydropower plants with substantial storage capacity, like the Haukland cascade managed by Dalane Kraft in Norway, exemplify the benefits of integrating machine learning and real-time optimization. Dalane Kraft aimed to leverage Haukland’s inherent flexibility to maximize financial returns while automating optimal dispatch operations. If you would like to see the impact of implementing machine learning inflow forecasting at Haukland, you can read more by scanning the QR code on the left.
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