This article takes you inside a real-world deployment where MLOps at the edge powers hourly solar radiation forecasts in a photovoltaic plant. By combining AI with real-time data flows, and cloud integration, the solution delivers higher prediction accuracy, faster response times, and optimized plant performance. Discover how edge intelligence is transforming solar forecasting.
In the rapidly evolving world of renewable energy, optimizing the performance of photovoltaic (PV) plants is a constant challenge, and a strategic imperative.
One of the most influential factors in this equation is solar radiation, the primary input for calculating expected energy output. Accurately forecasting solar radiation on an hourly basis unlocks new levels of operational efficiency.
1. Enhanced Energy Forecasting:
Accurate solar radiation predictions enable better energy output estimations, allowing operators to align generation with demand more precisely.
2. Improved Operational Efficiency:
Knowing in advance when radiation will dip or peak enables operators to schedule cleanings or maintenance tasks during low-irradiance periods, avoiding unnecessary downtime.
3. Maintenance Optimization:
Patterns in solar radiation data can flag potential performance issues early, leading to proactive maintenance and increased system longevity.
4. Better Grid Integration:
Forecasting radiation improves grid coordination, reducing overproduction risks and minimizing reliance on fossil-fuel-based energy reserves.
The model is trained on weather data sourced from OpenMeteo. Key variables include:
These features are carefully selected to train the model to predict global_tilted_irradiance_instant (W/m²), a direct proxy for energy production capacity.
The model is a deep neural network with:
Training is based on historical data, optimizing for accuracy in real-world deployment scenarios. The model is developed using Tensorflow framework and will be served in the Edge Node using TFX engine.
To ensure low latency and high reliability, the model is deployed on an edge node located at the PV plant. This local deployment is crucial for real-time responsiveness and resilience in disconnected scenarios.
To support the deployment of the solar irradiance predictor on the edge, a suite of integrated components works in harmony to manage data ingestion, processing, storage, visualization, and communication.
These building blocks form the backbone of the edge architecture, ensuring that data moves seamlessly and efficiently from source to insight while enabling robust, low-latency operations even in offline scenarios.
The solar radiation prediction system operates through a coordinated, multi-stage pipeline that captures, processes, stores, and analyzes weather data in near real-time. This pipeline ensures that predictions are both timely and actionable, supporting the day-to-day decisions of PV plant operators. Below is an overview of how data flows through the system, from acquisition to visualization.
One of the strengths of this architecture is its modularity.
You can leverage any machine learning framework best suited for your needs, including: Edge Impulse models, frameworks compatible with Seldon ML, XGBoost, ONNX, TensorFlow, PyTorch, or Scikit-learn.
Similarly, the system is designed to send predictions and insights to virtually any cloud solution, such as: Google Cloud, AWS, Azure, Splunk, InfluxDB, MongoDB, Elastic, or Kafka.
This flexibility allows organizations to customize their infrastructure and tools without being locked into a single ecosystem, making the solution highly adaptable to diverse environments and use cases.
This edge-based MLOps approach empowers PV plant operators to:
This use case illustrates the power of MLOps at the edge. By combining AI, real-time data pipelines, and cloud integration, we’ve built a robust system for hourly solar radiation prediction in a PV plant. The result is improved forecasting, greater operational flexibility, and enhanced system performance.
As industrial AI moves to the edge, so must the infrastructure that supports it. The Barbara Edge AI Platform delivers the tools needed to operationalize Machine Learning across complex, distributed environments, securely, efficiently, and at scale. Whether you're just starting your AI journey or scaling from pilot to production, Barbara makes Edge MLOps a reality.
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