Renewable

The enterprise drone market is ascending rapidly. Goldman Sachs estimated that businesses will spend $13 billion on drones between now and 2020. Promising commercial applications for drones range from emergency response and firefighting to surveying farmland and grocery delivery. However, as is the case with any new and innovative technology, there have been some speed bumps along the way that must be delicately navigated before broad adoption sets in. One of the most common speed bumps for businesses is the challenge of analyzing the vast volumes of data collected by drones.

The energy industry is undergoing a rapid transformation in recent past owing to the enhanced role of renewables and enhanced data-driven models making the value chain smarter. In the context of the primary constituents of this sector comprising of coal, power, renewables, solar energy, oil, and gas, there is a huge role AI can play. The biggest disruption in power in recent times is in the smart grid which is quite flexible in comparison to the traditional grid. AI can be a huge enabler in the form of providing optimal configurations etc to create a really smart and efficient grid. By thorough analysis of data related to losses AI can help prevent transmission and distribution losses.

Training artificial intelligence is an energy intensive process. New estimates suggest that the carbon footprint of training a single AI is as much as 284 tonnes of carbon dioxide equivalent – five times the lifetime emissions of an average car. Emma Strubell at the University of Massachusetts Amherst in the US and colleagues have assessed the energy consumption required to train four large neural networks, a type of AI used for processing language. Language-processing AIs underpin the algorithms that power Google Translate as well as OpenAI's GPT-2 text generator, which can convincingly pen fake news articles when given a few lines of text. These AIs are trained via deep learning, which involves processing vasts amounts of data.

The pace of innovation is incredibly fast with new things being discovered daily. This is a special type of intelligence that is exhibited by computers and other machines. It's a flexible agent that perceives its environment and takes the necessary action required for the success of that particular phenomenon. Artificial intelligence is used when machines copy the cognitive functions of the human brain in learning and solving problems. As machines become increasingly capable, other facilities are removed from the definition.

Fixed term contract until 1 March 2021 The Royal College of Art is the UK's only entirely postgraduate art and design university. In 2018/19 the College will have some 2,300 students registered for MA, MRes, MPhil and PhD degrees and over 450 permanent academic, technical and administrative staff, with more than 1,000 visiting lecturers and professors. The RCA Robotics Laboratory, recently established and directed by RCA's Academic Leader in Robotics, Dr Sina Sareh, develops new bioinspired technologies for robot mobility, manipulation and attachment in unstructured and extreme environments through funded projects by EPSRC, Innovate UK and industrial partners. Following the Royal College of Art's Strategic Plan 2016-2021, the lab is intended to create significant research and education capacity in robotics by 2020, to support the RCA's ambitious expansion plans in Battersea South including a new robotics facility and new research centres - the most radical transformation of the institution's campus in its 181-year history. Through the Innovate UK's "Robotics and AI: Inspect, Maintain and Repair in Extreme Environments" funding scheme, a research project grant entitled Multi-Platform Inspection, Maintenance & Repair in Extreme Environments (MIMRee) has been awarded to the RCA.

University of Southern California (Los Angeles, CA): Developing novel data-driven predictive models for integration into real-time fault detection and diagnosis, and integrate those models by using predictive control algorithms to improve the efficiency of energy production operations in a geothermal power plant. The project will develop deep dynamic neural networks for fault prediction and predictive process control workflows to improve the efficiency of geothermal operations. Upflow Limited (Taupo, New Zealand): Making available multiple decades of closely-guarded production data from one of the world's longest operating geothermal fields, and combining it with the archives from the largest geothermal company operating in the U.S. Models developed from this massive data store will enable the creation of a prediction/recommendation engine that will help operators improve plant availability. Colorado School of Mines (Golden, CO): Applying new machine learning techniques to analyze remote-sensing images, with the goal of developing a process to identify the presence of blind geothermal resources based on surface characteristics. Colorado School of Mines will develop a methodology to automatically label data from hyperspectral images of Brady's Hot Springs, Desert Rock, and the Salton Sea.

The energy output a photo voltaic(PV) panel is a function of solar irradiation and weather parameters like temperature and wind speed etc. A general measure for solar irradiation called Global Horizontal Irradiance (GHI), customarily reported in Watt/meter$^2$, is a generic indicator for this intermittent energy resource. An accurate prediction of GHI is necessary for reliable grid integration of the renewable as well as for power market trading. While some machine learning techniques are well introduced along with the traditional time-series forecasting techniques, deep-learning techniques remains less explored for the task at hand. In this paper we give deep learning models suitable for sequence to sequence prediction of GHI. The deep learning models are reported for short-term forecasting $\{1-24\}$ hour along with the state-of-the art techniques like Gradient Boosted Regression Trees(GBRT) and Feed Forward Neural Networks(FFNN). We have checked that spatio-temporal features like wind direction, wind speed and GHI of neighboring location improves the prediction accuracy of the deep learning models significantly. Among the various sequence-to-sequence encoder-decoder models LSTM performed superior, handling short-comings of the state-of-the-art techniques.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Soft-bubble, "a highly compliant dense geometry tactile sensor for robot manipulation," is a sort of combined gripper and 3D camera that uses a soft membrane to grasp and image objects at the same time. HAPS Mobile, a SoftBank-backed company, is developing a high-altitude pseudo satellite: a massive, solar-powered, long endurance drone that acts like a much cheaper and more versatile satellite over a smaller area.

Verv, the Google-mentored energy tech startup behind the smart energy hub and green electricity sharing platform, recently announced that it has raised over £6.5 million (€7.5 million) in its Series A round led by environmental fund Earthworm. Earthworm has invested £5 million in Verv's pioneering IoT and renewable energy trading technology that could drive down household electricity bills and carbon emissions by over 20%. Other investors in the round include European innovation engine for sustainable energy, InnoEnergy, Crowdcube and international energy and services company, Centrica. Earthworm's investment is an important backing of Verv's vision to make millions of homes more green with a global network of smart hubs that offer a real-time breakdown of key appliance use and spend, as well as enable the trading of domestic renewable energy between communities. At Earthworm we are driven by sustainability and Verv represents a brilliant example of'enabling' technology.

The increased usage of solar energy places additional importance on forecasts of solar radiation. Solar panel power production is primarily driven by the amount of solar radiation and it is therefore important to have accurate forecasts of solar radiation. Accurate forecasts that also give information on the forecast uncertainties can help users of solar energy to make better solar radiation based decisions related to the stability of the electrical grid. To achieve this, we apply statistical post-processing techniques that determine relationships between observations of global radiation (made within the KNMI network of automatic weather stations in the Netherlands) and forecasts of various meteorological variables from the numerical weather prediction (NWP) model HARMONIE-AROME (HA) and the atmospheric composition model CAMS. Those relationships are used to produce probabilistic forecasts of global radiation. We compare 7 different statistical post-processing methods, consisting of two parametric and five non-parametric methods. We find that all methods are able to generate probabilistic forecasts that improve the raw global radiation forecast from HA according to the root mean squared error (on the median) and the potential economic value. Additionally, we show how important the predictors are in the different regression methods. We also compare the regression methods using various probabilistic scoring metrics, namely the continuous ranked probability skill score, the Brier skill score and reliability diagrams. We find that quantile regression and generalized random forests generally perform best. In (near) clear sky conditions the non-parametric methods have more skill than the parametric ones.