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 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.
Hugging Face Trending Papers
Aug 11
XGBoost "is all you need": the case of forecasting transmitted heat energy in District Heating Systems
This paper presents a comparative study of two distinct approaches, XGBoost and Long-Short Term Memory (LSTM), for forecasting transmitted heat energy in District Heating Systems (DHS). The objective is to explore scenarios in which conventional ML algorithms demonstrate better performance over deep learning networks in time series forecasting and the associated benefits in terms of computational cost and environmental impact.
arXiv AI
Aug 12
Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprint
arXiv:2608. 09998v1 Announce Type: new Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have become powerful tools for supporting and automating complex human tasks.