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.


Bandit-Based Solar Panel Control

AAAI Conferences

Solar panels sustainably harvest energy from the sun. To improve performance, panels are often equipped with a tracking mechanism that computes the sun’s position in the sky throughout the day. Based on the tracker’s estimate of the sun’s location, a controller orients the panel to minimize the angle of incidence between solar radiant energy and the photovoltaic cells on the surface of the panel, increasing total energy harvested. Prior work has developed efficient tracking algorithms that accurately compute the sun’s location to facilitate solar tracking and control. However, always pointing a panel directly at the sun does not account for diffuse irradiance in the sky, reflected irradiance from the ground and surrounding surfaces, power required to reorient the panel, shading effects from neighboring panels and foliage, or changing weather conditions (such as clouds), all of which are contributing factors to the total energy harvested by a fleet of solar panels. In this work, we show that a bandit-based approach can increase the total energy harvested by solar panels by learning to dynamically account for such other factors. Our contribution is threefold: (1) the development of a test bed based on typical solar and irradiance models for experimenting with solar panel control using a variety of learning methods, (2) simulated validation that bandit algorithms can effectively learn to control solar panels, and (3) the design and construction of an intelligent solar panel prototype that learns to angle itself using bandit algorithms.


Quantum Computers Tackle Big Data With Machine Learning

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Every two seconds, sensors measuring the United States' electrical grid collect 3 petabytes of data – the equivalent of 3 million gigabytes. Data analysis on that scale is a challenge when crucial information is stored in an inaccessible database. But researchers at Purdue University are working on a solution, combining quantum algorithms with classical computing on small-scale quantum computers to speed up database accessibility. They are using data from the U.S. Department of Energy National Labs' sensors, called phasor measurement units, that collect information on the electrical power grid about voltages, currents and power generation. Because these values can vary, keeping the power grid stable involves continuously monitoring the sensors.


Quantum computers tackle big data with machine learning

#artificialintelligence

Every two seconds, sensors measuring the United States' electrical grid collect 3 petabytes of data – the equivalent of 3 million gigabytes. Data analysis on that scale is a challenge when crucial information is stored in an inaccessible database. But researchers at Purdue University are working on a solution, combining quantum algorithms with classical computing on small-scale quantum computers to speed up database accessibility. They are using data from the U.S. Department of Energy National Labs' sensors, called phasor measurement units, that collect information on the electrical power grid about voltages, currents and power generation. Because these values can vary, keeping the power grid stable involves continuously monitoring the sensors.


Quantum computers tackle big data with machine learning

#artificialintelligence

WEST LAFAYETTE, Ind. -- Every two seconds, sensors measuring the United States' electrical grid collect 3 petabytes of data – the equivalent of 3 million gigabytes. Data analysis on that scale is a challenge when crucial information is stored in an inaccessible database. But researchers at Purdue University are working on a solution, combining quantum algorithms with classical computing on small-scale quantum computers to speed up database accessibility. They are using data from the U.S. Department of Energy National Labs' sensors, called phasor measurement units, that collect information on the electrical power grid about voltages, currents and power generation. Because these values can vary, keeping the power grid stable involves continuously monitoring the sensors.