arXiv AI
Improving Energy Efficiency of Oil Platforms Through Optimal Loading of Diesel Generators Using Machine Learning and Search Algorithms
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arXiv AI
Sep 10
Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts
arXiv:2609.06194v1 Announce Type: new Abstract: Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns....
Hugging Face Trending Papers
Sep 5
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
Oct 5
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.
Hugging Face Trending Papers
Oct 14
Two-stage Residual Learning for Modelling Strength-Informed Optimisation of Thermal Systems
Aggregate performance metrics do not identify local modelling inaccuracies in machine learning (ML) surrogates used for thermal-system optimisation. This study assesses local modelling strength throug...
arXiv Machine Learning
Jul 22
Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data
arXiv:2607. 19054v1 Announce Type: new Abstract: In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses during vehicle operations.
Hugging Face Trending Papers
Jul 21
Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data
In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses during vehicle operations. Our results show that Bayesian linear regression based on this physics-aware model can improve the reliability of the expected energy consumption, as compared with standard linear regression.