Since it began its rise to ubiquitous commodity back in the last part of the 19th century, electrical energy has played an essential role in the development of our modern society. It has fostered industrial changes, given birth to countless new products and services, and basically reshaped our culture. Simply put, it's impossible to conceive our lives without it. That's why so many researchers and experts are trying to look for new ways to optimize its generation and consumption. Today, we are still using contaminating power stations, we have lots of bad habits that make us waste energy, and then there's climate change, which is putting pressure on our energy demand.

Among the most exciting and fast-moving areas of the utility sector is the increasing prevalence of artificial intelligence (AI), machine learning, and similar digital tools across each facet of power production, transmission, and delivery. In previous months, the Energy Central Hot Topic Special Issues have focused on fields like Blockchain, GIS, DERs, and Energy Efficiency, but some of the most compelling pilots and programs happening in each of those areas would be impossible without AI technology integrated into energy systems. Because of that, it's time to let artificial shine with its own Hot Topic Special Issue! In partnership with Bidgley, the theme for our next special issue is "Brave New World: AI and Machine Learning at Utilities," and we want to hear from you! These advanced technologies are accomplishing so much these days, from enabling demand response programs to be optimized, integrating smart grid technologies across the network, empowering managed charging of electric vehicles, helping generators plan ahead for what future demand will look like, and so much more.

In the early 1980s, presentations about Infosys began with the founders' pointing out India and Bengaluru on a world map. Today, globally listed companies such as Dr Reddy's, Tata Motors, and Reliance Industries have made that redundant. The country is also the third-largest startup nation. A number of its business-to-consumer (B2C) ventures, from e-commerce major Flipkart to ride-sharing platform Ola, are known across the world. Now, a new wave of business-to-business (B2B) startups in niche segments is silently creating a significant impact globally.

AI and data analytics will allow the utility firms to optimize the management of customer data and connect with the customers at a deeper level. FREMONT, CA: Public utility companies have failed in maintaining customer experience levels. The inability to live up to the customers' expectations can be partly attributed to the lack of competition in the sector. However, there is another aspect to it too. Public utility companies had conventionally utilized the legacy systems that are slow and lack the efficiency required to meet the current service demands.

This work provides a review of this literature. The motivation for this summary arose from our companion paper Ruf and W ang [2019]. There we continue th e discussions of this note; in particular, of potentially problematic data leakage when training ANNs to historic financial data. This paper is organised in the following way. Section 2 featu res Table 1, a summary of the literature that concerns the use of ANNs for nonparametric pricing (and hedging) of options. Section 3 provides a list of recommended papers from Table 1. Section 4 provides a n overview of related work where ANNs are applied in the context of option pricing and hedging, but not necessarily as nonparametric estimation tools. Section 5 briefly discusses various regularisation techniq ues used in the reviewed literature.

--Hydropower plants are one of the most convenient option for power generation, as they generate energy exploiting a renewable source, they have relatively low operating and maintenance costs, and they may be used to provide ancillary services, exploiting the large reservoirs of available water . The recent advances in Information and Communication T echnologies (ICT) and in machine learning methodologies are seen as fundamental enablers to upgrade and modernize the current operation of most hydropower plants, in terms of condition monitoring, early diagnostics and eventually predictive maintenance. While very few works, or running technologies, have been documented so far for the hydro case, in this paper we propose a novel Key Performance Indicator (KPI) that we have recently developed and tested on operating hydropower plants. In particular, we show that after more than one year of operation it has been able to identify several faults, and to support the operation and maintenance tasks of plant operators. S power generation from renewable sources is increasingly seen as a fundamental component in a joint effort to support decarbonization strategies, hydroelectric power generation is experiencing a new golden age. In fact, hydropower has a number of advantages compared to other types of power generation from renewable sources. Most notably, hydropower generation can be ramped up and down, which provides a valuable source of flexibility for the power grid, for instance, to support the integration of power generation from other renewable energy sources, like wind and solar. In addition, water in hydropower plants' large reservoirs may be seen as an energy storage resource in low-demand periods and transformed into electricity when needed [1], [2].

We present the Compressive Transformer, an attentive sequence model which compresses past memories for long-range sequence learning. We find the Com-pressive Transformer obtains state-of-the-art language modelling results in the WikiText-103 and Enwik8 benchmarks, achieving 17. 1 ppl and 0. 97 bpc respectively. We also find it can model high-frequency speech effectively and can be used as a memory mechanism for RL, demonstrated on an object matching task. To promote the domain of long-range sequence learning, we propose a new open-vocabulary language modelling benchmark derived from books, PG-19. Humans have a remarkable ability to remember information over long time horizons. When reading a book, we build up a compressed representation of the past narrative, such as the characters and events that have built up the story so far. We can do this even if they are separated by thousands of words from the current text, or long stretches of time between readings. During daily life, we make use of memories at varying timescales: from locating the car keys, placed in the morning, to recalling the name of an old friend from decades ago. These feats of memorisation are not achieved by storing every sensory glimpse throughout one's lifetime, but via lossy compression. We aggressively select, filter, or integrate input stimuli based on factors of surprise, perceived danger, or repetition -- amongst other signals (Richards and Frankland, 2017). Memory systems in artificial neural networks began with very compact representations of the past. Recurrent neural networks (RNNs, Rumelhart et al. (1986)) learn to represent the history of observations in a compressed state vector. The state is compressed because it uses far less space than the history of observations -- the model only preserving information that is pertinent to the optimization of the loss.

The chief executive of technology giant IBM has urged Australian business leaders to plan for dramatic changes to their organisations and the workforce caused by artificial intelligence and advances in quantum computing. Ginni Rometty, who has run the $US120 billion company since 2012 and is in Australia to sign an AI deal with Woodside Petroleum and attend other customer meetings, said business leaders must change their approach to hiring or risk being left behind. Ms Rometty gave a keynote address at a cloud computing conference in Sydney on Tuesday, alongside Woodside chief executive Peter Coleman who told the event he expects AI technology to slice maintenance costs in its plants by 30 per cent a year - or around $300 million. Mr Coleman said this is one of several applications being developed with IBM that also include automating production and improving cybersecurity. Westpac Banking Corp chief executive Brian Hartzer also spoke at the event.

When it comes to hyped technologies that could aid in the corporate fight against climate change, the blockchain and artificial intelligence are probably neck-in-neck. For those who view both with skepticism, I would suggest there's one big difference in the adoption cycle of the two. I can cite numerous pilot projects involving blockchain, but I think it's reasonable to doubt how it will scale. AI, on the other hand, is already driving some very real-world corporate progress toward stated sustainability goals, particularly energy efficiency. Exhibit A is the warehouse operation I wrote about in October, Lineage Logistics, which is using machine learning combined with sensors and data about weather and other parameters to reduce the amount of electricity it uses to keep food frozen. So far, it has cut annual power consumption by 33 million kilowatt-hours, saving $4 million along the way.

At the vast Pernis refinery in Rotterdam, where Royal Dutch Shell processes 20m tonnes of crude oil a year, any glitches or unplanned downtime can be costly. The equipment and operating conditions at Europe's largest refinery are monitored using 50,000 sensors that generate 100,000 measurements a minute. Last year Shell started using machine learning to better analyse and process that data. The model was designed to predict failures in control valves, and it allowed workers to carry out maintenance or adjust operating conditions as needed. The work at Pernis is an example of how oil and gas companies use artificial intelligence and machine learning to notice problems before they occur, sometimes months in advance.