A machine learning approach for wind turbine power forecasting for maintenance planning
importance 5/10
ai assisted
Energy Load and Power Forecasting wind energy stage: research
Problem
Wind turbine power output is hard to predict, making it difficult to schedule maintenance without losing energy production.
Why it matters
Better forecasting aligns maintenance with low-wind periods, improving grid stability and renewable energy output.
What the AI did
The study compared nine machine learning methods (interpretable, explainable, and blackbox models) for forecasting wind turbine power production.
Results
not stated in source
Impact
The evidence only supports that ML forecasting methods were compared; no measured outcome is stated.
Record
| Venue | Energy Informatics |
|---|---|
| Published | 2025 |
| Work type | article |
| Authors | Hariom Dhungana |
| Citations | 41 (OpenAlex, at import time) |
| References | 38 |
| Model or method | Linear Regression, KNN, XGBoost, Random Forest, graphical neural network; blackbox model not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1186/s42162-024-00459-4 |
| Open access | gold |
| Keywords | wind turbine, power forecasting, machine learning, maintenance planning, renewable energy |
Importance rating
5
real but narrower advance
Wind forecasting supports grid stability and renewable output per abstract, but no quantified scale (capacity, turbines, savings) is given; moderate industry relevance.
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