Wind


Bayesian Alignments of Warped Multi-Output Gaussian Processes

Neural Information Processing Systems

We propose a novel Bayesian approach to modelling nonlinear alignments of time series based on latent shared information. We apply the method to the real-world problem of finding common structure in the sensor data of wind turbines introduced by the underlying latent and turbulent wind field. The proposed model allows for both arbitrary alignments of the inputs and non-parametric output warpings to transform the observations. This gives rise to multiple deep Gaussian process models connected via latent generating processes. We present an efficient variational approximation based on nested variational compression and show how the model can be used to extract shared information between dependent time series, recovering an interpretable functional decomposition of the learning problem.


Fugro using machine learning to map boulders on the sea floor ZDNet

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Geo-data firm Fugro collects and analyses information about the Earth and the structures built upon it. It surveys the land and in the case of mapping objects on the sea floor, Fugro uses side scan sonar, collected via boats, to gather information. One project sees Fugro search the sea for boulders to help its customers determine whether they can set up an offshore windfarm. "Windfarm companies want to know where the impediments and where the potential sites they can build windfarms are," Fugro senior innovation engineer Marcus Nepveaux said, speaking at AWS re:Invent in Las Vegas. "So we go in, we map the sea floor for them, tell them where the big rocks or the little rocks are … they may be as small as a foot, and as big as we can detect."


Fukushima farmland that became unusable in 2011 is being converted into wind and solar power plants

Daily Mail - Science & tech

Farmland in Fukushima that was rendered unusable after the disastrous 2011 nuclear meltdown is getting a second chance at productivity. A group of Japanese investors have created a new plan to use the abandoned land to build wind and solar power plants, to be used to send electricity to Tokyo. The plan calls for the construction of eleven solar power plants and ten wind power plants, at an estimated cost of $2.75 billion. Fukushima has been aggressively converting land damaged by the 2011 meltdown, such as this golf course (pictured above) into a source of renewable energy. A new $2.75 billion plan will add eleven new solar plants and ten wind power plants to former farmland The project is expected to be completed in March of 2024 and is backed by a group of investors, including Development Bank of Japan and Mizuho Bank.


Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

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Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult.


Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

#artificialintelligence

Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult.


Robotic inspectors developed to fix wind farms

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Fully autonomous robots that are able to inspect damaged wind farms have been developed by Scots scientists. Unlike most drones, they don't require a human operator and could end the need for technicians to abseil down turbines to carry out repairs. The multi-million pound project is showing how the bots can walk, dive, fly and even think for themselves. They're being developed by Orca - the Offshore Robotics for Certification of Assets hub. The hub bills itself as the largest academic centre of its kind in the world and is led from Heriot-Watt and Edinburgh universities through its Centre for Robotics.


AI Starts to Live Up to Its Energy Efficiency Potential

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Perhaps no technology has generated more hype in recent years than artificial intelligence (AI). In some industries, it is certainly living up to it.


A Simulation of UAV Power Optimization via Reinforcement Learning

arXiv.org Artificial Intelligence

This paper demonstrates a reinforcement learning approach to the optimization of power consumption in a UAV system in a simplified data collection task. Here, the architecture consists of two common reinforcement learning algorithms, Q-learning and Sarsa, which are implemented through a combination of robot operating system (ROS) and Gazebo. The effect of wind as an influential factor was simulated. The implemented algorithm resulted in reasonable adjustment of UAV actions to the wind field in order to minimize its power consumption during task completion over the domain.


Determining offshore wind installation times using machine learning and open data

arXiv.org Machine Learning

The installation process of offshore wind turbines requires the use of expensive jack-up vessels. These vessels regularly report their position via the Automatic Identification System (AIS). This paper introduces a novel approach of applying machine learning to AIS data from jack-up vessels. We apply the new method to 13 offshore wind farms in Danish, German and British waters. For each of the wind farms we identify individual turbine locations, individual installation times, time in transit and time in harbor for the respective vessel. This is done in an automated way exclusively using AIS data with no prior knowledge of turbine locations, thus enabling a detailed description of the entire installation process.


Clir Renewables uses AI to analyze, understand and predict wind farm behavior

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In recent years many use the term along with machine learning to describe developments in the capabilities of software programs and machinery.