Automating the design of urban environments via digital twinning software, moving from sustainable to circular economies and integrating micro-mobility or Mobility 2.0 into the transport mix are among the "strategy shifts" cities need to make, according to ABI Research. The analyst company also warns that a shift from "safe and secure cities" to "resilient cities" and a rethinking of the urban environment through smart spaces will be required. In its new whitepaper, 5 Ways Smart Cities Are Getting Smarter, ABI Research highlights that while smart city tech investments will reach over $61 billion globally in 2026, most of the expenditure will be for incremental improvements. "It is an illusion to believe that adding just a shallow layer of IoT (Internet of Things) technology to legacy urban environments will allow cities to address the urban challenges of the future, ranging from the provision of sustainable energy to the adoption of smart mobility and the construction of resilient cities," says Dominique Bonte, vice president at ABI Research. As they prepare to face new threats such as cyber-attacks and climate change, Bonte said this "new reality" requires new approaches, leveraging a range of new technologies to create true strategy shifts.

Life for millions of energy consumers in the United States came to a grinding halt several times in the last few years due to large-scale power blackouts caused by forest fires. Transmission and distribution lines and critical infrastructure belonging to utilities are spread over thousands of miles, often, through poorly accessible wilderness. Overgrown vegetation and dead trees can touch and fall on power lines causing break downs and short circuits. They can also cause forest fires, and when they go unchecked, flare up into major ones. The vegetation across thousands of miles requires constant monitoring, pruning, and maintenance to ensure the right-of-way is constantly maintained.

Scientists have built simulations to help explain behavior in the real world, including modeling for disease transmission and prevention, autonomous vehicles, climate science, and in the search for the fundamental secrets of the universe. But how to interpret vast volumes of experimental data in terms of these detailed simulations remains a key challenge. Probabilistic programming offers a solution--essentially reverse-engineering the simulation--but this technique has long been limited due to the need to rewrite the simulation in custom computer languages, plus the intense computing power required. To address this challenge, a multinational collaboration of researchers using computing resources at Lawrence Berkeley National Laboratory's National Energy Research Scientific Computing Center (NERSC) has developed the first probabilistic programming framework capable of controlling existing simulators and running at large-scale on HPC platforms. The system, called Etalumis ("simulate" spelled backwards), was developed by a group of scientists from the University of Oxford, University of British Columbia (UBC), Intel, New York University, CERN, and NERSC as part of a Big Data Center project.

Scientists from the School of Energy and Power Engineering, Chongqing University, China, have discovered a highly efficient, time saving as well as a reliable machine learning (ML) method for the research and development of novel organic photovoltaic (OPV) materials. During the development of high performing OPV materials, if one can pre-establish the correlation between the structure of the designed material and its photovoltaic property, it becomes highly meaningful and time saving. The research is reported in the journal Science Advances. OPV cells are an easy and highly economical method for transforming the solar energy into electrical energy. Until now, the typical OPV materials-based research has focused on building a relationship between the newly developed OPV molecular material and its organic photovoltaic material properties.

A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials. Trained with materials from the ICSD database, our GAN model can generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The percentage of chemically valid (charge neutral and electronegativity balanced) samples out of all generated ones reaches 84.5% by our GAN when trained with materials from ICSD even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules. Our algorithm could be used to speed up inverse design or computational screening of inorganic materials.

Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g. In this context, as solar generators are geographically dispersed and ground measurements are not always easy to obtain, it is very important to have general models that can predict solar irradiance without the need of local data. In this paper, a model that can perform short-term forecasting of solar irradiance in any general location without the need of ground measurements is proposed. To do so, the model considers satellite-based measurements and weather-based forecasts, and employs a deep neural network structure that is able to generalize across locations; particularly, the network is trained only using a small subset of sites where ground data is available, and the model is able to generalize to a much larger number of locations where ground data does not exist. As a case study, 25 locations in The Netherlands are considered and the proposed model is compared against four local models that are individually trained for each location using ground measurements. Despite the general nature of the model, it is shown show that the proposed model is equal or better than the local models: when comparing the average performance across all the locations and prediction horizons, the proposed model obtains a 31.31% Introduction With the increasing integration of renewable sources into the electrical grid, accurate forecasting of renewable source generation has become one of the most important challenges across several applications. Among them, balancing the electrical grid via activation of reserves is arguably one of the most critical ones to ensure a stable system. In particular, due to their intermittent and unpredictable nature, the more renewables are integrated, the more complex the grid management becomes [1, 2]. This is the postprint of the article: Short-term forecasting of solar irradiance without local telemetry: a generalized model using satellite data, Solar Energy 173 (2018), 566-577 . Corresponding author Email address: j.lagogarcia@tudelft.nl (Jesus Lago) In particular, in addition to activation of reserves to manage the grid stability, short-term forecasts of solar irradiance are paramount for operational planning, switching sources, programming backup, short-term power trading, peak load matching, scheduling of power systems, congestion management, and cost reduction [2-4]. Solar irradiance forecasting The forecasting of solar irradiance can be typically divided between methods for global horizontal irradiance (GHI) and methods for direct normal irradiance (DNI) [5], with the latter being a component of the GHI (together with the diffuse solar irradiance). As in this work GHI is forecasted, [5] should be used for a complete review on methods for DNI.

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.

In this pattern, learn how to create and deploy deep learning models by using a Jupyter Notebook in an IBM Watson Studio environment. You also create deep learning experiments with hyperparameters optimization by using a Watson Studio GUI for monitoring different runs, then select the best model for deployment. Computer vision is on the rise, and there might be scenarios where a machine must classify images based on their class to aid in the decision-making process. In this code pattern, we demonstrate how to do multiclass classification (with three classes) by using IBM Watson Studio and IBM Deep Learning as a Service. We use yoga postures data to identify the class given an image.

Natural gas is an important energy source for much of our industrial, heating and electricity needs. The price of natural gas can fluctuate greatly. I made a time series analysis with external regressors to investigate how well modeling could forecast the price of natural gas. Using data from the US Energy Information Administration, I acquired monthly pricing data for Natural Gas from January of 1990 until present. I also acquired data on a number of related energy features.

Artificial Intelligence, or AI for short, is nothing new; it goes way back to the 1950s. But things are different now; the vast volumes of data and the computing capabilities we have today mean we can do things better. So, what pain points are utilities seeing today that AI can help with? Let me share some examples. My first is about how AI can help optimize aging production capabilities while, at the same time, minimizing maintenance costs.