arXiv AI
Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts
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Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts
Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns. Accurate wind turbine power predictions can ident...
arXiv AI
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Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study
arXiv:2607. 14024v1 Announce Type: cross Abstract: With rising global energy demand and growing awareness of climate change and its impacts, the share of renewable energies in the global energy mix continues to grow.
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Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study
With rising global energy demand and growing awareness of climate change and its impacts, the share of renewable energies in the global energy mix continues to grow. Unlike conventional power generation, the output of renewable energy sources cannot be controlled as consistently due to their dependence on environmental conditions.
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Technical Comparative Benchmarking Study: Advanced AI Hybrid Methods for Renewable Energy Farm Optimization and Forecasting
The paper benchmarks a range of AI methods—conventional ML, ensemble learning, deep neural networks, recurrent architectures, Transformers, graph models, and hybrid ensemble deep learning—on three renewable energy datasets, including large‑scale wave energy converter (WEC) data and wind farm SCADA measurements. Tree ensembles, particularly Extra Trees, outperform traditional ML and neural predictors on structured WEC layout data, achieving a 63.7% MAE reduction over an MLP baseline. Spatial‑temporal graph networks (STGCN) and an RF‑BiLSTM hybrid further improve forecasting accuracy, with the hybrid model reaching an MAE of 150.5 kW, a 75% reduction over a standalone LSTM and 10% better than STGCN. The study concludes that no single architecture dominates; randomized ensembles excel for structured surrogate modeling, graph networks for explicit spatial interactions, and hybrid recurrent ensembles for combined nonlinear tabular and temporal dynamics.
arXiv AI
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Automated Feature Engineering, AutoML, and Decision-Focused Learning for Improved Energy Consumption Forecasting
The thesis presents a domain‑specific automated feature engineering pipeline, AutoEnergy, for energy consumption forecasting. AutoEnergy generates interpretable features from timestamps and lagged consumption, integrates with AutoML, and achieves 19.52%–84.72% error reductions across eighteen real‑world datasets while running 1.31–4.41 times faster than baseline methods. When combined with Decision‑Focused Learning for battery storage, it cuts operating costs by 22.9%–56.5% on a UK property dataset.
arXiv Machine Learning
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WPBench: A Comprehensive Benchmark for Wind Power Forecasting
arXiv:2609.24444v1 Announce Type: new Abstract: Accurate, reliable, and deployable wind power forecasting is critical for power system dispatch, renewable energy integration, and electricity market o...