The "Mayflower 400", the world's first intelligent ship, bobs gently in a light swell as it stops its engines in Plymouth Sound, off England's southwest coast, before self-activating a hydrophone designed to listen to whales. The 50-foot (15-metre) trimaran, which weighs nine tonnes and navigates with complete autonomy, is preparing for a transatlantic voyage. On its journey, the vessel, covered in solar panels, will study marine pollution and analyse plastic in the water, as well as track aquatic mammals. Eighty per cent of the underwater world remains unexplored. Brett Phaneuf, the co-founder of the charity ProMare and the mastermind behind the Mayflower project, said the ocean exerts "the most powerful force" on the global climate.
Nasa's InSight Mars lander is currently trying to endure the abrasive Martian environment, as it sits on the Red Planet conserving power as its solar panels get covered in dust. InSight was designed to be powered by solar energy, gathered through dual two-meter panels. It was always expected that the panels would reduce their power output as time went on and dust landed on them, but would still have enough to last throughout the two-year mission. Unfortunately, not all has gone to plan. Despite InSight landing in Elysium Planitia, a windswept area of Mars that gets lots of sunlight, none of the passing dust devils (funnel-like chimneys of hot air) have been close enough to clean the panels.
In a September 2020 essay in Nature Energy, three scientists posed several "grand challenges" -- one of which was to find suitable materials for thermal energy storage devices that could be used in concert with solar energy systems. Fortuitously, Mingda Li -- the Norman C. Rasmussen Assistant Professor of Nuclear Science and Engineering at MIT, who heads the department's Quantum Matter Group -- was already thinking along similar lines. In fact, Li and nine collaborators (from MIT, Lawrence Berkeley National Laboratory, and Argonne National Laboratory) were developing a new methodology, involving a novel machine-learning approach, that would make it faster and easier to identify materials with favorable properties for thermal energy storage and other uses. The results of their investigation appear this month in a paper for Advanced Science. "This is a revolutionary approach that promises to accelerate the design of new functional materials," comments physicist Jaime Fernandez-Baca, a distinguished staff member at Oak Ridge National Laboratory.
Materials called perovskites are widely heralded as a likely replacement for silicon as the material of choice for solar cells, but their greatest drawback is their tendency to degrade relatively rapidly. Over recent years, the usable lifetime of perovskite-based cells has gradually improved from minutes to months, but it still lags far behind the decades expected from silicon, the material currently used for virtually all commercial solar panels. Now, an international interdisciplinary team led by MIT has come up with a new approach to narrowing the search for the best candidates for long-lasting perovskite formulations, out of a vast number of potential combinations. Already, their system has zeroed in on one composition that in the lab has improved on existing versions more than tenfold. Even under real-world conditions at full solar cell level, beyond just a small sample in a lab, this type of perovskite has performed three times better than the state-of-the-art formulations.
Energy harvesting offers an attractive and promising mechanism to power low-energy devices. However, it alone is insufficient to enable an energy-neutral operation, which can eliminate tedious battery charging and replacement requirements. Achieving an energy-neutral operation is challenging since the uncertainties in harvested energy undermine the quality of service requirements. To address this challenge, we present a rollout-based runtime energy-allocation framework that optimizes the utility of the target device under energy constraints. The proposed framework uses an efficient iterative algorithm to compute initial energy allocations at the beginning of a day. The initial allocations are then corrected at every interval to compensate for the deviations from the expected energy harvesting pattern. We evaluate this framework using solar and motion energy harvesting modalities and American Time Use Survey data from 4772 different users. Compared to state-of-the-art techniques, the proposed framework achieves 34.6% higher utility even under energy-limited scenarios. Moreover, measurements on a wearable device prototype show that the proposed framework has less than 0.1% energy overhead compared to iterative approaches with a negligible loss in utility.
A pizza box sized solar panel in orbit is producing enough electricity to power an iPad, according to a succesful test of the technology by the US Navy. The Photovoltaic Radiofrequency Antenna Module (PRAM) was launched in May 2020 attached to a drone that loops around the Earth every 90 minutes and is designed to harness light from the sun to convert to electricity. The 12x12 inch panel is an early experiment for a technology that could one day harness solar radiation from the sun and beam it to anywhere on the Earth. It is designed to make the best use of light in space, which doesn't have to pass through the atmosphere where it loses energy before reaching the ground. The Pentagon one day envisages an array of panels in space that could send power to even the most remote parts of the planet and create a new global power grid.
His opinion lends credence to the assumption made in this article, namely that renewable energy, artificial intelligence (AI) and machine learning (ML) are not only intricately linked, but that'smart' renewable energy must be considered the most sustainable way forward for our energy needs. And it will increasingly continue to do so. Along with allied technologies such as ML, deep learning and advanced neural networks, AI has transformative potential for the global energy sector. An important caveat regarding the fossil fuel-renewable energy dichotomy will also be discussed at article's conclusion. ML is already entrenched in our everyday lives, from smart phone assistants like Apple's Siri or Samsung's Bixby, to voice and image recognition systems. More important even will be ML's ability to assist in tackling some of the world's most pressing physical and logistical problems, including that pertaining to the full potential of renewable energy.
Solar power installations are becoming common in residential and commercial areas, largely due to their decreasing costs. However, the power system is vulnerable to some anomalies such as rainstorm or hurricane, which cost greatly to restoration. As a result, detecting and predicting abnormal events from the spatialtemporal series plays a vital role in the solar system, aiming to capture the variety of intrinsic reasons for the anomalies. For example, the rainstorm and drought would bring out different types and patterns of anomalies. In many cases, the abnormal event will also start at one location and then propagate to its neighbors with a time delay, leading to spatial-temporal correlation among anomalies. Thus it is crucial to make observations at multiple locations, which correspondingly form the spatial-temporal series. In this paper, we address non-stationarity and strong spatial-temporal correlation through the following contributions: - Strong spatial-temporal correlation: We present a spatial-temporal Bernoulli process (also extended to categorical observations), which is proposed by . The model can flexibly capture the spatial-temporal correlations and interactions without assuming time-decaying influence. It can also efficiently make predictions for any location at any future time for timely ramp event detection.
The liberalization process of the energy sector and global Organization of the Petroleum Exporting Countries (OPEC) crisis in the 1970s are two major drivers of the decentralization and decarbonization energy generation systems. Distributed energy systems, especially renewable energy sources (RES), have become more economically viable, and their market share has significantly increased in the last two decades. Wind and solar energy plants are the most prominent RES, which generates a fluctuating and weather dependent power output. Power systems are operated according to certain national and international norms where the voltage and frequency parameters should not exceed certain operational boundaries. Power networks are also designed to carry specific maximum power capacities. The power output characteristics of RES increase the vulnerability and uncertainty levels of power systems, which makes it challenging for the power systems operators to integrate higher amounts of RES into their control zones. Energy forecasting is one of the most promising methods which increases the operational capabilities of RES. Wind and solar forecasting algorithms have been used for two decades by various energy market players, such as utilities, RES plant operators, and power traders. Transmission and distribution system operators use energy forecasting algorithms to schedule their daily energy generation profiles, thus minimizing last-minute balancing power needs.
Rapid urbanization, increasing integration of distributed renewable energy resources, energy storage, and electric vehicles introduce new challenges for the power grid. In the US, buildings represent about 70% of the total electricity demand and demand response has the potential for reducing peaks of electricity by about 20%. Unlocking this potential requires control systems that operate on distributed systems, ideally data-driven and model-free. For this, reinforcement learning (RL) algorithms have gained increased interest in the past years. However, research in RL for demand response has been lacking the level of standardization that propelled the enormous progress in RL research in the computer science community. To remedy this, we created CityLearn, an OpenAI Gym Environment which allows researchers to implement, share, replicate, and compare their implementations of RL for demand response. Here, we discuss this environment and The CityLearn Challenge, a RL competition we organized to propel further progress in this field.