Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Forecasting methods in energy planning models
KB Debnath, M Mourshed - Renewable and Sustainable Energy Reviews, 2018 - Elsevier
Energy planning models (EPMs) play an indispensable role in policy formulation and energy
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
Numerical study on efficiency and robustness of wave energy converter-power take-off system for compressed air energy storage
G Chen, R Kuang, W Li, K Cui, D Fu, Z Yang, Z Liu… - Renewable Energy, 2024 - Elsevier
The unpredictable fluctuations of wave lead to an imbalance between energy supply and
demand. This article proposes a wave-driven compressed air energy storage system, which …
demand. This article proposes a wave-driven compressed air energy storage system, which …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
[HTML][HTML] A review of machine learning and deep learning applications in wave energy forecasting and WEC optimization
Ocean energy technologies are in their developmental stages, like other renewable energy
sources. To be useable in the energy market, most components of wave energy devices …
sources. To be useable in the energy market, most components of wave energy devices …
A data-driven multi-model methodology with deep feature selection for short-term wind forecasting
With the growing wind penetration into the power system worldwide, improving wind power
forecasting accuracy is becoming increasingly important to ensure continued economic and …
forecasting accuracy is becoming increasingly important to ensure continued economic and …
Geothermal 4.0: AI-enabled geothermal reservoir development-current status, potentials, limitations, and ways forward
The development and operation of geothermal resources are known to be capital-intensive
due to their remote geographical location and extreme reservoir pressure & temperature …
due to their remote geographical location and extreme reservoir pressure & temperature …
Electricity production, capacity factor, and plant efficiency index at the Mutriku wave farm (2014–2016)
Mutriku has recently become the first commercial wave farm to release its operating data.
The plant has 14 OWC operating turbines, and this study has conducted an analysis of …
The plant has 14 OWC operating turbines, and this study has conducted an analysis of …
Physical laws meet machine intelligence: current developments and future directions
The advent of technology including big data has allowed machine learning technology to
strengthen its place in solving different science and engineering complex problems …
strengthen its place in solving different science and engineering complex problems …
Forecasting, hindcasting and feature selection of ocean waves via recurrent and sequence-to-sequence networks
This article explores the concepts of ocean wave multivariate multistep forecasting,
reconstruction and feature selection. We introduce recurrent neural network frameworks …
reconstruction and feature selection. We introduce recurrent neural network frameworks …